NEST Simulator

739 found.

Jiang HJ, Qi G, Duarte R, Feldmeyer D and van Albada SJ (2024) [A layered microcircuit model of somatosensory cortex with three interneuron types and cell-type-specific short-term plasticity. ] Oxford University Press (OUP) Cerebral Cortex. 34 [url] [doi]
Tiddia G, Sergi L and Golosio B (2024) [Theoretical framework for learning through structural plasticity. ] American Physical Society (APS) Physical Review E. 110 [url] [doi]
Igarashi J (2024) [Future projections for mammalian whole-brain simulations based on technological trends in related fields. ] Elsevier BV Neuroscience Research [url] [doi]
Ayuso‐Martinez A, Casanueva‐Morato D, Dominguez‐Morales JP., Indiveri G, Jimenez‐Fernandez A and Jimenez‐Moreno G (2024) [Analog Implementation of a Spiking System for Performing Arithmetic Logic Operations on Mixed‐Signal Neuromorphic Processors. ] Wiley Advanced Intelligent Systems [url] [doi]
Zhang W, Ma S, Ji X, Liu X, Cong Y and Shi L (2024) [The development of general-purpose brain-inspired computing. ] Springer Science and Business Media LLC Nature Electronics. 7:954–965 [url] [doi]
Pronold J, van Meegen A, Shimoura RO, Vollenbröker H, Senden M, Hilgetag CC, Bakker R and van Albada SJ (2024) [Multi-scale spiking network model of human cerebral cortex. ] Oxford University Press (OUP) Cerebral Cortex. 34 [url] [doi]
Nembhani P, Vadivel K, Tang G, Tahghighi M, van Schaik GJ, Sifalakis M, Al-Ars Z and Yousefzadeh A (2024) [SENSIM: An Event-driven Parallel Simulator for Multi-core Neuromorphic Systems. ], 2024 International Joint Conference on Neural Networks (IJCNN). IEEE:1–6 [url] [doi]
Pedersen JE., Abreu S, Jobst M, Lenz G, Fra V, Bauer FC, Muir DR, Zhou P, Vogginger B, Heckel K, Urgese G, Shankar S, Stewart TC., Sheik S and Eshraghian JK. (2024) [Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing. ] Springer Science and Business Media LLC Nature Communications. 15 [url] [doi]
Jin Z, Cai J, Di Y, Lei Y, Fu Y, Liu Y and Yang X (2024) [Adaptive structure generation and neuronal differentiation for memory encoding in SNNs. ] Elsevier BV Neurocomputing. 610:128470 [url] [doi]
Merger C, Albers J, Honerkamp C and Helias M (2024) [Spurious self-feedback of mean-field predictions inflates infection curves. ] American Physical Society (APS) Physical Review E. 110 [url] [doi]
Ding J and Huang T (2024) [Towards human-leveled vision systems. ] Springer Science and Business Media LLC Science China Technological Sciences. 67:2331–2349 [url] [doi]
Stanojevic A, Woźniak S, Bellec G, Cherubini G, Pantazi A and Gerstner W (2024) [High-performance deep spiking neural networks with 0.3 spikes per neuron. ] Springer Science and Business Media LLC Nature Communications. 15 [url] [doi]
Senk J, Hagen E, van Albada SJ and Diesmann M (2024) [Reconciliation of weak pairwise spike–train correlations and highly coherent local field potentials across space. ] Oxford University Press (OUP) Cerebral Cortex. 34 [url] [doi]
Morrison A and Diesmann M (2008) [Maintaining causality in discrete time neuronal network simulations. ] Springer-Verlag Berlin and Heidelberg GmbH & Co. KG:267-278 [url] [doi]
Schirner M, Domide L, Perdikis D, Triebkorn P, Stefanovski L, Pai R, Prodan P, Valean B, Palmer J, Langford C, Blickensdörfer A, van der Vlag M, Diaz-Pier S, Peyser A, Klijn W, Pleiter D, Nahm A, Schmid O, Woodman M, Zehl L, Fousek J, Petkoski S, Kusch L, Hashemi M, Marinazzo D, Mangin JF, Flöel A, Akintoye S, Stahl BC, Cepic M, Johnson E, Deco G, McIntosh AR, Hilgetag CC, Morgan M, Schuller B, Upton A, McMurtrie C, Dickscheid T, Bjaalie JG, Amunts K, Mersmann J, Jirsa V and Ritter P (2022) [Brain simulation as a cloud service: The Virtual Brain on EBRAINS. ] NeuroImage. 251:118973 [url] [doi]
Dominguez-Morales JP., Buccelli S, Gutierrez-Galan D, Colombi I, Jimenez-Fernandez A and Chiappalone M (2021) [Real-time detection of bursts in neuronal cultures using a neuromorphic auditory sensor and spiking neural networks. ] Neurocomputing. 449:422-434 [url] [doi]
Romano V, Reddington AL., Cazzanelli S, Mazza R, Ma Y, Strydis C, Negrello M, Bosman LW.J. and De Zeeuw CI. (2020) [Functional Convergence of Autonomic and Sensorimotor Processing in the Lateral Cerebellum. ] Cell Reports. 32:107867 [url] [doi]
Wybo WA, Jordan J, Ellenberger B, Mengual UM, Nevian T and Senn W (2021) [Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses. ] eLife. 10:e60936 [url] [doi]
Dai K, Gratiy SL., Billeh YN., Xu R, Cai B, Cain N, Rimehaug AE., Stasik AJ., Einevoll GT., Mihalas S, Koch C and Arkhipov A (2020) [Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits. ] PLOS Computational Biology. 16:e1008386 [url] [doi]
Lehtimäki M, Seppälä I, Paunonen L and Linne ML (2020) [Accelerated Simulation of a Neuronal Population via Mathematical Model Order Reduction. ], 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). :118-122 [url] [doi]
Rhodes O, Peres L, Rowley AG. D., Gait A, Plana LA., Brenninkmeijer C and Furber SB. (2019) [Real-time cortical simulation on neuromorphic hardware. ] The Royal Society Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 378:20190160 [url] [doi]
LaTorre A, Kwong MT, García-Grajales JA., Shi R, Jérusalem A and Peña JM (2019) [Model calibration using a parallel differential evolution algorithm in computational neuroscience: simulation of stretch induced nerve deficit. ] Elsevier BV Journal of Computational Science:101053 [url] [doi]
Hazan H, Saunders DJ., Sanghavi DT., Siegelmann H and Kozma R (2019) [Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data. ] Springer Science and Business Media LLC Annals of Mathematics and Artificial Intelligence. 88:1237–1260 [url] [doi]
Yin Y (2021) [RANDOM NEURAL NETWORK METHODS AND DEEP LEARNING. ] Cambridge University Press (CUP) Probability in the Engineering and Informational Sciences:1–31 [url] [doi]
Vannucci L., Ambrosano A., Cauli N., Albanese U., Falotico E., Ulbrich S., Pfotzer L., Hinkel G., Denninger O., Peppicelli D., Guyot L., Von Arnim A., Deser S., Maier P., Dillman R., Klinker G., Levi P., Knoll A., Gewaltig M.-O. and Laschi C. (2015) A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation. , Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on. :1179–1184 [doi]
Ray S, Chintaluri C, Bhalla US. and Wójcik DK. (2016) NSDF: Neuroscience Simulation Data Format. Neuroinformatics. 14:147–167 [doi]
Petrovici MA (2016) Artificial Brains: Simulation and Emulation of Neural Networks. Springer International Publishing:59–81 [doi]
Baptista D'io and Morgado-Dias F (2013) A survey of artificial neural network training tools. Springer-Verlag Neural Computing and Applications. 10:1–7 [doi]
Marc-Oliver Gewaltig RC (2012) [Current practice in software development for computational neuroscience and how to improve it. ] Public Library of Science PLOS Computational Biology [url] [doi]
Beaubois R, Cheslet J, Duenki T, De Venuto G, Carè M, Khoyratee F, Chiappalone M, Branchereau P, Ikeuchi Y and Levi T (2024) [BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network. ] Springer Science and Business Media LLC Nature Communications. 15 [url] [doi]
Snyder S, Clerico V, Cong G, Kulkarni S, Schuman C, Risbud S and Parsa M (2024) [Transductive Spiking Graph Neural Networks for Loihi. ], Proceedings of the Great Lakes Symposium on VLSI 2024. ACM [url] [doi]
Fitz H, Hagoort P and Petersson KM (2024) [Neurobiological Causal Models of Language Processing. ] MIT Press Neurobiology of Language. 5:225–247 [url] [doi]
Vieth M, Rahimi A, Gorgan Mohammadi A, Triesch J and Ganjtabesh M (2024) [Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch. ] Frontiers Media SA Frontiers in Neuroinformatics. 18 [url] [doi]
Kauth K, Stadtmann T, Sobhani V and Gemmeke T (2023) [neuroAIx: FPGA Cluster for Reproducible and Accelerated Neuroscience Simulations of SNNs. ], 2023 IEEE Nordic Circuits and Systems Conference (NorCAS). IEEE [url] [doi]
Gambosi B, Jamal Sheiban F, Biasizzo M, Antonietti A, D’angelo E, Mazzoni A and Pedrocchi A (2024) [A Model with Dopamine Depletion in Basal Ganglia and Cerebellum Predicts Changes in Thalamocortical Beta Oscillations. ] World Scientific Pub Co Pte Ltd International Journal of Neural Systems. 34 [url] [doi]
Robens M, Kleijnen R, Schiek M and van Waasen S (2024) [NoC simulation steered by NEST: McAERsim and a Noxim patch. ] Frontiers Media SA Frontiers in Neuroscience. 18 [url] [doi]
Steiner LA., Crompton D, Sumarac S, Vetkas A, Germann J, Scherer M, Justich M, Boutet A, Popovic MR., Hodaie M, Kalia SK., Fasano A, Hutchison WD WD., Lozano AM., Lankarany M, Kühn AA. and Milosevic L (2024) [Neural signatures of indirect pathway activity during subthalamic stimulation in Parkinson’s disease. ] Springer Science and Business Media LLC Nature Communications. 15 [url] [doi]
Layer M, Helias M and Dahmen D (2024) [Effect of Synaptic Heterogeneity on Neuronal Coordination. ] American Physical Society (APS) PRX Life. 2 [url] [doi]
Kusch L, Diaz-Pier S, Klijn W, Sontheimer K, Bernard C, Morrison A and Jirsa V (2024) [Multiscale co-simulation design pattern for neuroscience applications. ] Frontiers Media SA Frontiers in Neuroinformatics. 18 [url] [doi]
Jimenez Romero C, Yegenoglu A, Pérez Martín A, Diaz-Pier S and Morrison A (2023) [Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networks. ] Springer Science and Business Media LLC Swarm Intelligence. 18:1–29 [url] [doi]
Jones A, Gandhi V, Mahiddine AY. and Huyck C (2023) [Bridging Neuroscience and Robotics: Spiking Neural Networks in Action. ] MDPI AG Sensors. 23:8880 [url] [doi]
Hong C, Yuan M, Zhang M, Wang X, Zhang C, Wang J, Pan G and Tang H (2024) [SPAIC: A Spike-Based Artificial Intelligence Computing Framework. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Computational Intelligence Magazine. 19:51–65 [url] [doi]
Kulkarni SR., Tabassum A, Lim SH, Schuman CD., Theilman BH., Rothganger F, Wang F and Aimone JB. (2024) [Explaining Neural Spike Activity for Simulated Bio-plausible Network through Deep Sequence Learning. ], 2024 Neuro Inspired Computational Elements Conference (NICE). IEEE [url] [doi]
王银 (2024) [Design and Evaluation of Virtual Simulation Technology in Neurophysiology Teaching Methods. ] Hans Publishers Advances in Education. 14:665–669 [url] [doi]
Mohanta R and Assisi C (2022) [Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide. ] The Neurons Behavior Data Analysis and Theory collective Neurons, Behavior, Data analysis, and Theory. 1 [url] [doi]
Qu P, Ji XL, Chen JJ, Pang M, Li YC, Liu XY and Zhang YH (2024) [Research on General-Purpose Brain-Inspired Computing Systems. ] Springer Science and Business Media LLC Journal of Computer Science and Technology. 39:4–21 [url] [doi]
Miedema R and Strydis C (2024) [ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations. ] Frontiers Media SA Frontiers in Neuroinformatics. 18 [url] [doi]
Najafi T, Jaafar R, Remli R, W. Zaidi A and Chellappan K (2021) [The Role of Brain Signal Processing and Neuronal Modelling in Epilepsy – A Review. ] Penerbit Universiti Kebangsaan Malaysia (UKM Press) Jurnal Kejuruteraan. 33:801–815 [url] [doi]
Amato LG, Vergani AA, Lassi M, Fabbiani C, Mazzeo S, Burali R, Nacmias B, Sorbi S, Mannella R, Grippo A, Bessi V and Mazzoni A (2024) [Personalized modeling of Alzheimer’s disease progression estimates neurodegeneration severity from EEG recordings. ] Wiley Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring. 16 [url] [doi]
Islam R, Majurski P, Kwon J, Sharma A and Tummala SRSK (2024) [Benchmarking Artificial Neural Network Architectures for High-Performance Spiking Neural Networks. ] MDPI AG Sensors. 24:1329 [url] [doi]
Parziale A and Marcelli A (2024) [Understanding upper-limb movements via neurocomputational models of the sensorimotor system and neurorobotics: where we stand. ] Springer Science and Business Media LLC Artificial Intelligence Review. 57 [url] [doi]
Ganguly C, Bezugam SS, Abs E, Payvand M, Dey S and Suri M (2024) [Spike frequency adaptation: bridging neural models and neuromorphic applications. ] Springer Science and Business Media LLC Communications Engineering. 3 [url] [doi]
Gemo E, Spiga S and Brivio S (2024) [SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks. ] Frontiers Media SA Frontiers in Neuroscience. 17 [url] [doi]
Ayuso-Martinez A, Casanueva-Morato D, Dominguez-Morales J. P., Jimenez-Fernandez A and Jimenez-Moreno G (2023) [Construction of a Spike-Based Memory Using Neural-Like Logic Gates Based on Spiking Neural Networks on SpiNNaker. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Emerging Topics in Computing. 11:868–881 [url] [doi]
Bologna LL, Tocco A, Smiriglia R, Romani A, Schürmann F and Migliore M (2023) [Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub. ] Frontiers Media SA Frontiers in Neuroinformatics. 17 [url] [doi]
Gillet C, Vincent AF., Le Gal B and Saïghi S (2023) [A High-Level Methodology to Evaluate and Optimize Digital Architectures Targeting Spike Encoding. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Access. 11:120654–120665 [url] [doi]
Wang F, Kulkarni S, Theilman B, Rothganger F, Schuman C, Lim SH and Aimone JB (2024) [Scaling neural simulations in STACS. ] IOP Publishing Neuromorphic Computing and Engineering. 4:024002 [url] [doi]
Gandolfi D, Mapelli J, Solinas SM. G., Triebkorn P, D’Angelo E, Jirsa V and Migliore M (2023) [Full-scale scaffold model of the human hippocampus CA1 area. ] Springer Science and Business Media LLC Nature Computational Science. 3:264–276 [url] [doi]
Morrison A, Aertsen A and Diesmann M (2007) [Spike-Timing-Dependent Plasticity in Balanced Random Networks. ] MIT Press - Journals Neural Computation. 19:1437–1467 [url] [doi]
Bouhadjar Y, Siegel S, Tetzlaff T, Diesmann M, Waser R and Wouters DJ (2023) [Sequence learning in a spiking neuronal network with memristive synapses. ] IOP Publishing Neuromorphic Computing and Engineering. 3:034014 [url] [doi]
Kuniyoshi Y, Kuriyama R, Omura S, Gutierrez CE, Sun Z, Feldotto B, Albanese U, Knoll AC., Yamada T, Hirayama T, Morin FO., Igarashi J, Doya K and Yamazaki T (2023) [Embodied bidirectional simulation of a spiking cortico-basal ganglia-cerebellar-thalamic brain model and a mouse musculoskeletal body model distributed across computers including the supercomputer Fugaku. ] Frontiers Media SA Frontiers in Neurorobotics. 17 [url] [doi]
Fang W, Chen Y, Ding J, Yu Z, Masquelier T, Chen D, Huang L, Zhou H, Li G and Tian Y (2023) [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence. ] American Association for the Advancement of Science (AAAS) Science Advances. 9 [url] [doi]
Sønstebø JL., Brunborg H and Lepperød ME (2023) [Spikeometric: Linear Non-Linear Cascade Spiking Neural Networks with Pytorch Geometric. ] The Open Journal Journal of Open Source Software. 8:5451 [url] [doi]
Gruel A, Martinet J and Magno M (2023) [Simultaneous neuromorphic selection of multiple salient objects for event vision. ], 2023 International Joint Conference on Neural Networks (IJCNN). IEEE [url] [doi]
Pham MD, D'Angiulli A, Dehnavi MM and Chhabra R (2023) [From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?. ] MDPI AG Brain Sciences. 13:1316 [url] [doi]
Stanojevic A, Woźniak S, Bellec G, Cherubini G, Pantazi A and Gerstner W (2023) [An exact mapping from ReLU networks to spiking neural networks. ] Elsevier BV Neural Networks. 168:74–88 [url] [doi]
Trinh AT, Girardi-Schappo M, Béïque JC, Longtin A and Maler L (2023) [Adaptive spike threshold dynamics associated with sparse spiking of hilar mossy cells are captured by a simple model. ] Wiley The Journal of Physiology. 601:4397–4422 [url] [doi]
Knowles TC., Summerton AG., Whiting JG. H. and Pearson MJ. (2023) [Ring Attractors as the Basis of a Biomimetic Navigation System. ] MDPI AG Biomimetics. 8:399 [url] [doi]
Golosio B, Villamar J, Tiddia G, Pastorelli E, Stapmanns J, Fanti V, Paolucci PS, Morrison A and Senk J (2023) [Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices. ] MDPI AG Applied Sciences. 13:9598 [url] [doi]
Shirsavar SR, Vahabie AH and Dehaqani MRA. (2023) [Models developed for spiking neural networks. ] Elsevier BV MethodsX. 10:102157 [url] [doi]
Chlasta K, Sochaczewski P, Wójcik GM. and Krejtz I (2023) [Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds. ] Frontiers Media SA Frontiers in Neuroinformatics. 17 [url] [doi]
Cabrera-Álvarez J, Sánchez-Claros J, Carrasco-Gómez M, del Cerro-León A, Gómez-Ariza CJ., Maestú F, Mirasso CR. and Susi G (2023) [Understanding the effects of cortical gyrification in tACS: insights from experiments and computational models. ] Frontiers Media SA Frontiers in Neuroscience. 17 [url] [doi]
Wu Z, Shen Y, Zhang J, Liang H, Zhao R, Li H, Xiong J, Zhang X and Chua Y (2023) [BIDL: a brain-inspired deep learning framework for spatiotemporal processing. ] Frontiers Media SA Frontiers in Neuroscience. 17 [url] [doi]
Yang Y, Hosseini MJM, Kruger W and Nawrocki RA. (2023) [Modular Modeling of Analog Organic Neuromorphic Circuits: Toward Prototyping of Hardware-Level Spiking Neural Networks. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Circuits and Systems I: Regular Papers. 70:1161–1174 [url] [doi]
Kauth K, Stadtmann T, Sobhani V and Gemmeke T (2023) [neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20x faster than biological real-time. ] Frontiers Media SA Frontiers in Computational Neuroscience. 17 [url] [doi]
Ghazinouri B, Nejad MM and Cheng S (2023) [Navigation and the efficiency of spatial coding: insights from closed-loop simulations. ] Springer Science and Business Media LLC Brain Structure and Function [url] [doi]
Cerina M, Piastra MC and Frega M (2023) [The potential of in vitro neuronal networks cultured on micro electrode arrays for biomedical research. ] IOP Publishing Progress in Biomedical Engineering. 5:032002 [url] [doi]
Qu P, Lin H, Pang M, Liu X, Zheng W and Zhang Y (2023) [ENLARGE: An Efficient SNN Simulation Framework on GPU Clusters. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Parallel and Distributed Systems. 34:2529–2540 [url] [doi]
Frenkel C, Bol D and Indiveri G (2023) [Bottom-Up and Top-Down Approaches for the Design of Neuromorphic Processing Systems: Tradeoffs and Synergies Between Natural and Artificial Intelligence. ] Institute of Electrical and Electronics Engineers (IEEE) Proceedings of the IEEE. 111:623–652 [url] [doi]
Schmid D, Oess T and Neumann H (2023) [Listen to the Brain-Auditory Sound Source Localization in Neuromorphic Computing Architectures. ] MDPI AG Sensors. 23:4451 [url] [doi]
Siegel S, Bouhadjar Y, Tetzlaff T, Waser R, Dittmann R and Wouters DJ (2023) [System model of neuromorphic sequence learning on a memristive crossbar array. ] IOP Publishing Neuromorphic Computing and Engineering. 3:024002 [url] [doi]
Bouhadjar Y, Wouters DJ., Diesmann M and Tetzlaff T (2023) [Coherent noise enables probabilistic sequence replay in spiking neuronal networks. ] Public Library of Science (PLoS) PLOS Computational Biology. 19:e1010989 [url] [doi]
Zhang H and Vargas DV (2023) [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Access. 11:81033–81070 [url] [doi]
Zajzon B, Duarte R and Morrison A (2023) [Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning. ] Frontiers Media SA Frontiers in Integrative Neuroscience. 17 [url] [doi]
Angelidis E (2023) [A perspective on large-scale simulation as an enabler for novel biorobotics applications. ] Frontiers Media SA Frontiers in Robotics and AI. 10 [url] [doi]
Wang C, Zhang T, Chen X, He S, Li S and Wu S (2023) [BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming. ] eLife Sciences Publications, Ltd eLife. 12 [url] [doi]
Zeng Y, Zhao D, Zhao F, Shen G, Dong Y, Lu E, Zhang Q, Sun Y, Liang Q, Zhao Y, Zhao Z, Fang H, Wang Y, Li Y, Liu X, Du C, Kong Q, Ruan Z and Bi W (2023) [BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation. ] Elsevier BV Patterns:100789 [url] [doi]
Date P, Kulkarni S, Young A, Schuman C, Potok T and Vetter J (2023) [Encoding integers and rationals on neuromorphic computers using virtual neuron. ] Springer Science and Business Media LLC Scientific Reports. 13 [url] [doi]
Arthur BJ., Kim CM., Chen S, Preibisch S and Darshan R (2023) [A scalable implementation of the recursive least-squares algorithm for training spiking neural networks. ] Frontiers Media SA Frontiers in Neuroinformatics. 17 [url] [doi]
Giacopelli G, Migliore M and Tegolo D (2023) [NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation. ] MDPI AG Sensors. 23:4598 [url] [doi]
Haufler D, Ito S, Koch C and Arkhipov A (2023) [Simulations of cortical networks using spatially extended conductance-based neuronal models. ] Wiley The Journal of Physiology [url] [doi]
Lehmann HM., Hille J, Grassmann C, Knoll A and Issakov V (2022) [Analog Spiking Neural Network Based Phase Detector. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Circuits and Systems I: Regular Papers. 69:4837–4846 [url] [doi]
Aitsam M, Davies S and Nuovo AD (2022) [Neuromorphic Computing for Interactive Robotics: A Systematic Review. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Access. 10:122261–122279 [url] [doi]
Ronchini M, Zamani M, Huynh HA, Rezaeiyan Y, Panuccio G, Farkhani H and Moradi F (2021) [A CMOS-based neuromorphic device for seizure detection from LFP signals. ] IOP Publishing Journal of Physics D: Applied Physics. 55:014001 [url] [doi]
Nilsson M, Schelén O, Lindgren A, Bodin U, Paniagua C, Delsing J and Sandin F (2023) [Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions. ] Frontiers Media SA Frontiers in Neuroscience. 17:1074439 [url] [doi]
Zajzon B, Dahmen D, Morrison A and Duarte R (2023) [Signal denoising through topographic modularity of neural circuits. ] eLife Sciences Publications, Ltd eLife. 12:e77009 [url] [doi]
Wei W and Wang X (2022) [Ion-specific effects in confined nanochannels and neural network. ] Wiley Aggregate. 4:e302 [url] [doi]
Si H and Sun X (2022) [Inter-areal transmission of multiple neural signals through frequency-division-multiplexing communication. ] Springer Science and Business Media LLC Cognitive Neurodynamics [url] [doi]
Schmitt FJ, Rostami V and Nawrot MP (2023) [Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST. ] Frontiers Media SA Frontiers in Neuroinformatics. 17:941696 [url] [doi]
Birgiolas J, Haynes V, Gleeson P, Gerkin RC., Dietrich SW. and Crook S (2023) [NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML. ] Public Library of Science (PLoS) PLOS Computational Biology. 19:e1010941 [url] [doi]
Rosa-Gallardo DJ., Torre JCde la, Quintana FM., Dominguez-Morales JP. and Perez-Peña F (2023) [NESIM-RT: A real-time distributed spiking neural network simulator. ] Elsevier BV SoftwareX. 22:101349 [url] [doi]
Lee JH, Tsunada J, Vijayan S and Cohen YE. (2022) [Cortical circuit-based lossless neural integrator for perceptual decision-making: A computational modeling study. ] Frontiers in Computational Neuroscience. 16:979830 [url] [doi]
Sten S, Podéus H, Sundqvist N, Elinder F, Engström M and Cedersund G (2023) [A quantitative model for human neurovascular coupling with translated mechanisms from animals. ] Public Library of Science PLOS Computational Biology. 19:1-42 [url] [doi]
De Schepper R, Geminiani A, Masoli S, Rizza MF, Antonietti A, Casellato C and D’Angelo E (2022) [Model simulations unveil the structure-function-dynamics relationship of the cerebellar cortical microcircuit. ] Communications Biology. 5:1240 [url] [doi]
Cobb EA. W., Petroccione MA. and Scimemi A (2023) [NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON. ] Scientific Reports. 13:464 [url] [doi]
Leon PS, Knock SA., Woodman M. M, Domide L, Mersmann J, McIntosh AR. and Jirsa V (2013) [The Virtual Brain: a simulator of primate brain network dynamics. ] Frontiers Media SA Frontiers in Neuroinformatics. 7:10 [url] [doi]
Gutierrez CE, Skibbe H, Musset H and Doya K (2022) [A Spiking Neural Network Builder for Systematic Data-to-Model Workflow. ] Frontiers Media SA Frontiers in Neuroinformatics. 16:855765 [url] [doi]
Oberländer J, Bouhadjar Y and Morrison A (2022) [Learning and replaying spatiotemporal sequences: A replication study. ] Frontiers Media SA Frontiers in Integrative Neuroscience. 16:974177 [url] [doi]
Bologna LL, Smiriglia R, Lupascu CA, Appukuttan S, Davison AP., Ivaska G, Courcol JD and Migliore M (2022) [The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models. ] Frontiers Media SA Frontiers in Neuroinformatics. 16:991609 [url] [doi]
Griffiths JD., Shen K and Gleeson P (2022) [Editorial: Neuroinformatics of large-scale brain modelling. ] Frontiers Media SA Frontiers in Neuroinformatics. 16:1043732 [url] [doi]
Nieus T, Borgonovo D, Diwakar S, Aletti G and Naldi G (2022) [A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials. ] Frontiers Media SA Frontiers in Applied Mathematics and Statistics. 8:1023310 [url] [doi]
Morrison A, Straube S, Plesser HE and Diesmann M (2007) Exact subthreshold integration with continuous spike times in discrete-time neural network simulations. MIT Press Neural computation. 19:47–79 [doi]
Chrysanthidis N, Fiebig F, Lansner A and Herman P (2022) [Traces of Semantization, from Episodic to Semantic Memory in a Spiking Cortical Network Model. ] eNeuro. 9:eneuro.0062-22.2022 [url] [doi]
Yu Q, Li S, Tang H, Wang L, Dang J and Tan KC (2022) [Toward Efficient Processing and Learning With Spikes: New Approaches for Multispike Learning. ] IEEE Transactions on Cybernetics. 52:1364-1376 [url] [doi]
D'Angelo E and Jirsa V (2022) [The quest for multiscale brain modeling. ] Elsevier BV Trends in Neurosciences. 45:777–790 [url] [doi]
Trapani A, Sheiban FJ, Bertone E, Chiosso S, Colombo L, D'Andrea M, De Santis F, Fati F, Fossati V, Gonzalez V and Pedrocchi A (2022) [Reproducing a decision-making network in a virtual visual discrimination task. ] Frontiers in Integrative Neuroscience. 16:930326 [url] [doi]
Ramezanian-Panahi M, Abrevaya G, Gagnon-Audet JC, Voleti V, Rish I and Dumas G (2022) [Generative Models of Brain Dynamics. ] Frontiers in Artificial Intelligence. 5:807406 [url] [doi]
Li G and Yap PT (2022) [From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. ] Frontiers in Human Neuroscience. 16:940842 [url] [doi]
Feldotto B, Soare C, Knoll A, Sriya P, Astill S, de Kamps M and Chakrabarty S (2022) [Evaluating Muscle Synergies With EMG Data and Physics Simulation in the Neurorobotics Platform. ] Frontiers in Neurorobotics. 16:856797 [url] [doi]
Kon M and Francis G (2022) [Cortical circuits for top-down control of perceptual grouping. ] Neural Networks. 151:190-210 [url] [doi]
Hagen E, Magnusson SH., Ness TV., Halnes G, Babu PN., Linssen C, Morrison A and Einevoll GT. (2022) [Brain signal predictions from multi-scale networks using a linearized framework. ] PLoS Computational Biology. 18:e1010353 [url] [doi]
Shrestha A, Fang H, Mei Z, Rider DP, Wu Q and Qiu Q (2022) [A Survey on Neuromorphic Computing: Models and Hardware. ] IEEE Circuits and Systems Magazine. 22:6-35 [url] [doi]
Osborne H and de Kamps M (2022) [A numerical population density technique for N-dimensional neuron models. ] Frontiers in Neuroinformatics. 16:883796 [url] [doi]
Leone G, Raffo L and Meloni P (2022) [A Bandwidth-Efficient Emulator of Biologically-Relevant Spiking Neural Networks on FPGA. ] IEEE Access. 10:76780-76793 [url] [doi]
Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ, Schüttler L, Gramelsberger G, Diesmann M, Plesser HE. and Albada SJ. van (2022) [Connectivity concepts in neuronal network modeling. ] Public Library of Science (PLoS) PLOS Computational Biology. 18:e1010086 [url] [doi]
Pronold J., Jordan J., Wylie B.J.N., Kitayama I., Diesmann M. and Kunkel S. (2022) [Routing brain traffic through the von Neumann bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers. ] Parallel Computing. 113:102952 [url] [doi]
Tiddia G, Golosio B, Albers J, Senk J, Simula F, Pronold J, Fanti V, Pastorelli E, Paolucci PS and van Albada SJ. (2022) [Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster. ] Frontiers in Neuroinformatics. 16:883333 [url] [doi]
Yamazaki K, Vo-Ho VK, Bulsara D and Le N (2022) [Spiking Neural Networks and Their Applications: A Review. ] Brain Sciences. 12:863 [url] [doi]
Trensch G and Morrison A (2022) [A System-on-Chip Based Hybrid Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks. ] Frontiers in Neuroinformatics. 16:884033 [url] [doi]
Awile O, Kumbhar P, Cornu N, Dura-Bernal S, King JG, Lupton O, Magkanaris I, McDougal RA., Newton AJ. H., Pereira F, Săvulescu A, Carnevale NT., Lytton WW., Hines ML. and Schürmann F (2022) [Modernizing the NEURON Simulator for Sustainability, Portability, and Performance. ] Frontiers in Neuroinformatics. 16:884046 [url] [doi]
Bouhadjar Y, Diesmann M, Wouters DJ and Tetzlaff T (2022) [Sequence learning, prediction, and replay in networks of spiking neurons. ] PLoS computational biology. 18:e1010233 [url] [doi]
Ladd A, Kim KG, Balewski J, Bouchard K and Ben-Shalom R (2022) [Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models. ] Frontiers in Neuroinformatics. 16:882552 [url] [doi]
Geminiani A, Mockevičius A, D’Angelo E and Casellato C (2022) [Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model. ] Frontiers in Systems Neuroscience. 16:919761 [url] [doi]
Antonietti A, Geminiani A, Negri E, D'Angelo E, Casellato C and Pedrocchi A (2022) [Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System. ] Frontiers in Neurorobotics. 16:817948 [url] [doi]
Ostrau C, Klarhorst C, Thies M and Rückert U (2022) [Benchmarking Neuromorphic Hardware and Its Energy Expenditure. ] Frontiers in Neuroscience. 16:873935 [url] [doi]
Layer M, Senk J, Essink S, van Meegen A, Bos H and Helias M (2022) [NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models. ] Frontiers in Neuroinformatics. 16:835657 [url] [doi]
Yegenoglu A, Subramoney A, Hater T, Jimenez-Romero C, Klijn W, Martín AP, van der Vlag M, Herty M, Morrison A and Diaz-Pier S (2022) [Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn. ] Frontiers in Computational Neuroscience. 16:885207 [url] [doi]
Panagiotou S, Sidiropoulos H, Soudris D, Negrello M and Strydis C (2022) [EDEN: A High-Performance, General-Purpose, NeuroML-Based Neural Simulator. ] Frontiers in Neuroinformatics. 16:724336 [url] [doi]
Feldotto B, Eppler JM, Jimenez-Romero C, Bignamini C, Gutierrez CE, Albanese U, Retamino E, Vorobev V, Zolfaghari V, Upton A, Sun Z, Yamaura H, Heidarinejad M, Klijn W, Morrison A, Cruz F, McMurtrie C, Knoll AC., Igarashi J, Yamazaki T, Doya K and Morin FO. (2022) [Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure. ] Frontiers in Neuroinformatics. 16:884180 [url] [doi]
Müller E, Arnold E, Breitwieser O, Czierlinski M, Emmel A, Kaiser J, Mauch C, Schmitt S, Spilger P, Stock R, Stradmann Y, Weis J, Baumbach A, Billaudelle S, Cramer B, Ebert F, Göltz J, Ilmberger J, Karasenko V, Kleider M, Leibfried A, Pehle C and Schemmel J (2022) [A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware. ] Frontiers in Neuroscience. 16:884128 [url] [doi]
Lopes PH., Oliveira BC, de S. Souza AA and Blanco W (2022) [Mitigating Computer Limitations in Replicating Numerical Simulations of a Neural Network Model With Hodgkin-Huxley-Type Neurons. ] Frontiers in Neuroinformatics. 16:874234 [url] [doi]
Albers J, Pronold J, Kurth AC, Vennemo SB, Mood KH, Patronis A, Terhorst D, Jordan J, Kunkel S, Tetzlaff T, Diesmann M and Senk J (2022) [A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations. ] Frontiers in Neuroinformatics. 16:837549 [url] [doi]
Peres L and Rhodes O (2022) [Parallelization of Neural Processing on Neuromorphic Hardware. ] Frontiers in Neuroscience. 16:867027 [url] [doi]
Javanshir A, Nguyen TT, Mahmud M. A. P and Kouzani AZ. (2022) [Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks. ] Neural Computation. 34:1289-1328 [url] [doi]
Huang C, Zeldenrust F and Celikel T (2022) [Cortical Representation of Touch in Silico. ] Neuroinformatics:1-27 [url] [doi]
Feldotto B, Morin FO. and Knoll A (2022) [The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments. ] Frontiers in Neurorobotics. 16:856727 [url] [doi]
Petschenig H, Bisio M, Maschietto M, Leparulo A, Legenstein R and Vassanelli S (2022) [Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks. ] Frontiers in Neuroscience. 16:838054 [url] [doi]
Maith O, Dinkelbach HÜ, Baladron J, Vitay J and Hamker FH. (2022) [BOLD Monitoring in the Neural Simulator ANNarchy. ] Frontiers in Neuroinformatics. 16:790966 [url] [doi]
Qu P, Yang L, Zheng W and Zhang Y (2022) [A review of basic software for brain-inspired computing. ] CCF Transactions on High Performance Computing. 4:34-42 [url] [doi]
Turner JP, Knight JC, Subramanian A and Nowotny T (2022) [mlGeNN: accelerating SNN inference using GPU-enabled neural networks. ] Neuromorphic Computing and Engineering. 2:024002 [url] [doi]
Pronold J, Jordan J, Wylie BJ. N., Kitayama I, Diesmann M and Kunkel S (2022) [Routing Brain Traffic Through the Von Neumann Bottleneck: Parallel Sorting and Refactoring. ] Frontiers in Neuroinformatics. 15:785068 [url] [doi]
Kurth AC, Senk J, Terhorst D, Finnerty J and Diesmann M (2022) [Sub-realtime simulation of a neuronal network of natural density. ] Neuromorphic Computing and Engineering. 2:021001 [url] [doi]
Chakravarty K, Roy S, Sinha A, Nambu A, Chiken S, Kotaleski JH and Kumar A (2022) [Transient Response of Basal Ganglia Network in Healthy and Low-Dopamine State. ] eNeuro. 9:eneuro.0376-21.2022 [url] [doi]
Schuman CD., Kulkarni SR., Parsa M, Mitchell J. P, Date P and Kay B (2022) [Opportunities for neuromorphic computing algorithms and applications. ] Nature Computational Science. 2:10-19 [url] [doi]
Yang X, Lei Y, Wang M, Cai J, Wang M, Huan Z and Lin X (2022) [Evaluation of the Effect of the Dynamic Behavior and Topology Co-Learning of Neurons and Synapses on the Small-Sample Learning Ability of Spiking Neural Network. ] Brain Sciences. 12:139 [url] [doi]
Heittmann A, Psychou G, Trensch G, Cox CE., Wilcke WW., Diesmann M and Noll TG. (2022) [Simulating the Cortical Microcircuit Significantly Faster Than Real Time on the IBM INC-3000 Neural Supercomputer. ] Frontiers in Neuroscience. 15:728460 [url] [doi]
Dasbach S, Tetzlaff T, Diesmann M and Senk J (2021) Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution. Frontiers in Neuroscience. 15:757790 [doi]
Trimarco E, Mirino P and Caligiore D (2021) Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder. Frontiers in Systems Neuroscience. 15:666649 [doi]
Meamardoost S, Bhattacharya M, Hwang EJ, Komiyama T, Mewes C, Wang L, Zhang Y and Gunawan R (2021) FARCI: Fast and Robust Connectome Inference. Brain Sciences. 11:1556 [doi]
Spreizer S, Senk J, Rotter S, Diesmann M and Weyers B (2021) [NEST Desktop, an Educational Application for Neuroscience. ] eNeuro. 8:eneuro.0274-21.2021 [url] [doi]
Jordan J, Schmidt M, Senn W and Petrovici MA (2021) Evolving interpretable plasticity for spiking networks. eLife. 10:e66273 [doi]
Scheffer LK. and Meinertzhagen IA. (2021) [A connectome is not enough – what is still needed to understand the brain of Drosophila?. ] Journal of Experimental Biology. 224 [url] [doi]
Cakan C, Jajcay N and Obermayer K (2021) [neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling. ] Cognitive Computation:1-21 [url] [doi]
Steffen L, Koch R, Ulbrich S, Nitzsche S, Roennau A and Dillmann R (2021) [Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics. ] Frontiers in Neuroscience. 15:667011 [url] [doi]
Tsang IJ, Corradi F, Sifalakis M, Van Leekwijck W and Latré S (2021) Radar-Based Hand Gesture Recognition Using Spiking Neural Networks. Electronics. 10:1405 [doi]
Plesser HE, Diesmann M, Gewaltig MO and Morrison A (2015) [NEST: the Neural Simulation Tool. ] Springer New York:1849–1852 [url] [doi]
Plesser HE, Diesmann M, Gewaltig MO and Morrison A (2019) [NEST: The Neural Simulation Tool. ] Springer New York:1–3 [url] [doi]
Suarez E, Kunkel S, Küsters A, Plesser HE and Lippert T (2021) Modular Supercomputing for Neuroscience. Springer International Publishing:63–80
Kremkow J, Kumar A, Rotter S and Aertsen A (2007) [Emergence of population synchrony in a layered network of the cat visual cortex. ] Neurocomputing. 70:2069–2073 [url] [doi]
Martinez-Canada P, Morillas C, Nieves JL, Pino B and Pelayo F (2015) Computational Color Imaging: 5th International Workshop, CCIW 2015, Saint Etienne, France, March 24-26, 2015, Proceedings. Springer International Publishing:118–127 [doi]
Martinez-Canada P, Morillas C, Romero S and Pelayo F (2015) Modeling Retina Adaptation with Multiobjective Parameter Fitting. Springer International Publishing. 9095:175–184 [doi]
Martinez-Canada P, Morillas C, Pino B and Pelayo F (2015) Towards a Generic Simulation Tool of Retina Models. Springer International Publishing:47–57 [doi]
Scorcioni R, Hamilton DJ. and Ascoli GA. (2008) [Self-sustaining non-repetitive activity in a large scale neuronal-level model of the hippocampal circuit. ] Neural Networks. 21:1153–1163 [url] [doi]
Pastorelli E, Capone C, Simula F, Sanchez-Vives MV., Del Giudice P, Mattia M and Stanislao Paolucci P (2019) Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections. Frontiers in Systems Neuroscience:arXiv:1902.08410 [doi]
Albada SJ. van, Pronold J, Meegen Avan and Diesmann M (2021) [Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex. ] Springer International Publishing:47–59 [url] [doi]
Weidel P, Duarte R and Morrison A (2021) [Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks. ] Frontiers Media SA Frontiers in Computational Neuroscience. 15:543872 [url] [doi]
Dipietro L, Elkin-Frankston S, Ramos-Estebanez C and Wagner T (2021) [Supercomputing in the Study and Stimulation of the Brain. ] IGI Global:290–300 [url] [doi]
Schuman CD., Kay B, Date P, Kannan R, Sao P and Potok TE. (2021) [Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers. ], 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE [url] [doi]
Knight JC. and Nowotny T (2021) [Larger GPU-accelerated brain simulations with procedural connectivity. ] Springer Science and Business Media LLC Nature Computational Science. 1:136–142 [url] [doi]
Wardak A and Gong P (2021) [Fractional diffusion theory of balanced heterogeneous neural networks. ] American Physical Society (APS) Physical Review Research. 3:013083 [url] [doi]
Susi G, Garcés P, Paracone E, Cristini A, Salerno M, Maestú F and Pereda E (2021) [FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency. ] Springer Science and Business Media LLC Scientific Reports. 11:12160 [url] [doi]
Stapmanns J, Hahne J, Helias M, Bolten M, Diesmann M and Dahmen D (2021) [Event-Based Update of Synapses in Voltage-Based Learning Rules. ] Frontiers Media SA Frontiers in Neuroinformatics. 15:609147 [url] [doi]
Kasi SK, Das S and Biswas S (2021) [Energy-efficient event pattern recognition in wireless sensor networks using multilayer spiking neural networks. ] Springer Science and Business Media LLC Wireless Networks. 27:2039–2054 [url] [doi]
Shimoura RO, Pena RF. O., Lima V, Kamiji NL., Girardi-Schappo M and Roque AC. (2021) [Building a model of the brain: from detailed connectivity maps to network organization. ] Springer Science and Business Media LLC The European Physical Journal Special Topics. 230:2887–2909 [url] [doi]
Santos-Mayo A, Moratti S, Echegaray Jde and Susi G (2021) [A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity. ] MDPI AG Biology. 10:801 [url] [doi]
Zweifel NO., Bush NE., Abraham I, Murphey TD. and Hartmann MJ. Z. (2021) [A dynamical model for generating synthetic data to quantify active tactile sensing behavior in the rat. ] Proceedings of the National Academy of Sciences Proceedings of the National Academy of Sciences. 118:e2011905118 [url] [doi]
Mascaro ALA, Falotico E, Petkoski S, Pasquini M, Vannucci L, Tort-Colet N, Conti E, Resta F, Spalletti C, Ramalingasetty ST, Arnim Avon, Formento E, Angelidis E, Blixhavn CH, Leergaard TB, Caleo M, Destexhe A, Ijspeert A, Micera S, Laschi C, Jirsa V, Gewaltig MO and Pavone FS. (2020) [Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience. ] Cold Spring Harbor Laboratory Frontiers in Systems Neuroscience [url] [doi]
Elbez H, Benhaoua MK, Devienne P and Boulet P (2021) [VS2N : Interactive Dynamic Visualization and Analysis Tool for Spiking Neural Networks. ], 2021 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE [url] [doi]
Bologna LL., Smiriglia R, Curreri D and Migliore M (2021) [The EBRAINS NeuroFeatureExtract: An Online Resource for the Extraction of Neural Activity Features From Electrophysiological Data. ] Frontiers Media SA Frontiers in Neuroinformatics. 15:713899 [url] [doi]
Podobas A, Svedin M, Chien SW. D., Peng IB., Ravichandran NB, Herman P, Lansner A and Markidis S (2021) [StreamBrain. ], Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies. ACM [url] [doi]
Moon J, Wu Y, Zhu X and Lu WD. (2021) [Neural connectivity inference with spike-timing dependent plasticity network. ] Springer Science and Business Media LLC Science China Information Sciences. 64:160405 [url] [doi]
Romaro C, Najman FA, Lytton WW., Roque AC. and Dura-Bernal S (2021) [NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model. ] MIT Press - Journals Neural Computation. 33:1993–2032 [url] [doi]
Osborne H, Lai YM, Lepperød ME, Sichau D, Deutz L and Kamps Mde (2021) [MIIND : A Model-Agnostic Simulator of Neural Populations. ] Frontiers Media SA Frontiers in Neuroinformatics. 15:614881 [url] [doi]
Korolija N, Popović J and Bojović MM. (2021) [Introduction to Dataflow Computing. ] IGI Global:96–105 [url] [doi]
Schmuker M, Kupper R, Aertsen A, Wachtler T and Gewaltig MO (2021) [Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code. ] Springer Science and Business Media LLC Biological Cybernetics. 115:161–176 [url] [doi]
Angelidis E, Buchholz E, Arreguit J, Rougé A, Stewart T, Arnim Avon, Knoll A and Ijspeert A (2021) [A spiking central pattern generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards. ] IOP Publishing Neuromorphic Computing and Engineering. 1:014005 [url] [doi]
Schuman CD., Plank JS., Parsa M, Kulkarni SR., Skuda N and Mitchell J. P (2021) [A Software Framework for Comparing Training Approaches for Spiking Neuromorphic Systems. ], 2021 International Joint Conference on Neural Networks (IJCNN). IEEE [url] [doi]
García-Vico ÁM. and Herrera F (2021) [A Preliminary Analysis on Software Frameworks for the Development of Spiking Neural Networks. ] Springer International Publishing:564–575 [url] [doi]
Dazza M, Métens S, Monceau P and Bottani S (2021) [A novel methodology to describe neuronal networks activity reveals spatiotemporal recruitment dynamics of synchronous bursting states. ] Springer Science and Business Media LLC Journal of Computational Neuroscience. 49:375–394 [url] [doi]
Nguyen QAP, Andelfinger P, Tan WJ, Cai W and Knoll A (2021) [Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware. ] ACM Transactions on Modeling and Computer Simulation. 31:1-26 [url] [doi]
Mosbacher Y, Khoyratee F, Goldin M, Kanner S, Malakai Y, Silva M, Grassia F, Simon YB, Cortes J, Barzilai A, Levi T and Bonifazi P (2020) [Toward neuroprosthetic real-time communication from in silico to biological neuronal network via patterned optogenetic stimulation. ] Scientific Reports. 10:7512 [pdf] [doi]
Dai K, Hernando J, Billeh YN., Gratiy SL., Planas J, Davison AP., Dura-Bernal S, Gleeson P, Devresse A, Dichter BK., Gevaert M, King JG., Geit WA. H. V, Povolotsky AV., Muller E, Courcol JD and Arkhipov A (2020) [The SONATA data format for efficient description of large-scale network models. ] Public Library of Science (PLoS) PLOS Computational Biology. 16:e1007696 [url] [doi]
Martı́nez-Cancino R, Delorme A, Truong D, Artoni F, Kreutz-Delgado K, Sivagnanam S, Yoshimoto K, Majumdar A and Makeig S (2020) [The Open EEGLAB Portal Interface:High-Performance Computing with EEGLAB. ] Elsevier BV NeuroImage:116778 [url] [doi]
Aljalbout E, Walter F, Röhrbein F and Knoll A (2020) [Task-Independent Spiking Central Pattern Generator: A Learning-Based Approach. ] Springer Science and Business Media LLC Neural Processing Letters. 51:2751–2764 [url] [doi]
Billeh YN., Cai B, Gratiy SL., Dai K, Iyer R, Gouwens NW., Abbasi-Asl R, Jia X, Siegle JH., Olsen SR., Koch C, Mihalas S and Arkhipov A (2020) [Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex. ] Elsevier BV Neuron. 106:388–403.e18 [url] [doi]
Yamaura H, Igarashi J and Yamazaki T (2020) [Simulation of a Human-Scale Cerebellar Network Model on the K Computer. ] Frontiers Media SA Frontiers in Neuroinformatics. 14:16 [url] [doi]
Ojiugwo CN., Abdallah AB. and Thron C (2020) [Simulation of Biological Learning with Spiking Neural Networks. ] Springer International Publishing:207–227 [url] [doi]
Michaelis C, Lehr AB. and Tetzlaff C (2020) [Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi. ] Frontiers in Neurorobotics. 14:589532 [url] [doi]
Yang X, Lin J, Zheng W, Zhao J, Ji M, Lei Y and Chai Z (2020) [Research on learning mechanism designing for equilibrated bipolar spiking neural networks. ] Springer Science and Business Media LLC Artificial Intelligence Review. 53:5189–5215 [url] [doi]
Kuriyama R, Casellato C, D'Angelo E and Yamazaki T (2021) [Real-Time Simulation of a Cerebellar Scaffold Model on Graphics Processing Units. ] Frontiers in Cellular Neuroscience. 15:623552 [url] [doi]
Knight JC., Komissarov A and Nowotny T (2021) [PyGeNN: A Python Library for GPU-Enhanced Neural Networks. ] Frontiers in Neuroinformatics. 15:659005 [url] [doi]
Brank B, Nassyr S, Pouyan F and Pleiter D (2020) [Porting Applications to Arm-based Processors. ], 2020 IEEE International Conference on Cluster Computing (CLUSTER). :559-566 [url] [doi]
George R, Chiappalone M, Giugliano M, Levi T, Vassanelli S, Partzsch J and Mayr C (2020) [Plasticity and Adaptation in Neuromorphic Biohybrid Systems. ] iScience. 23:101589 [url] [doi]
Steffen L, da Silva RK, Ulbrich S, Tieck JCV, Roennau A and Dillmann R (2020) [Networks of Place Cells for Representing 3D Environments and Path Planning. ], 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). :1158-1165 [url] [doi]
DeWolf T, Jaworski P and Eliasmith C (2020) [Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics. ] Frontiers in Neurorobotics. 14:568359 [url] [doi]
Rostro-Gonzalez H, Lauterio-Cruz JP and Pottiez O (2020) [Modelling Neural Dynamics with Optics: A New Approach to Simulate Spiking Neurons through an Asynchronous Laser. ] Electronics. 9:1853 [url] [doi]
Balaji A, Catthoor F, Das A, Wu Y, Huynh K, Dell-Anna FG., Indiveri G, Krichmar JL., Dutt ND. and Schaafsma S (2020) [Mapping Spiking Neural Networks to Neuromorphic Hardware. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 28:76–86 [url] [doi]
Yamazaki T, Igarashi J and Yamaura H (2021) [Human-scale brain simulation via supercomputer: a case study on the cerebellum. ] Neuroscience. 462:235-246 [url] [doi]
Caligiore D and Mirino P (2020) [How the Cerebellum and Prefrontal Cortex Cooperate During Trace Eyeblinking Conditioning. ] International Journal of Neural Systems. 30:2050041 [url] [doi]
Huyck CR and Vergani AA (2020) [Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons. ] Journal of Computational Neuroscience. 48:299-316 [url] [doi]
Qu P, Zhang Y, Fei X and Zheng W (2020) [High Performance Simulation of Spiking Neural Network on GPGPUs. ] IEEE Transactions on Parallel and Distributed Systems. 31:2510-2523 [url] [doi]
(2021) [Handbook of Research on Methodologies and Applications of Supercomputing. ] [url] [doi]
Rezaei H, Aertsen A, Kumar A and Valizadeh A (2020) [Facilitating the propagation of spiking activity in feedforward networks by including feedback. ] PLoS Computational Biology. 16:e1008033 [url] [doi]
Vlag MA. van der, Smaragdos G, Al-Ars Z and Strydis C (2020) [Exploring Complex Brain-Simulation Workloads on Multi-GPU Deployments. ] Association for Computing Machinery (ACM) ACM Transactions on Architecture and Code Optimization. 16:1–25 [url] [doi]
Mascaro ALA, Falotico E, Petkoski S, Pasquini M, Vannucci L, Tort-Colet N, Conti E, Resta F, Spalletti C, Ramalingasetty ST, von Arnim A, Formento E, Angelidis E, Blixhavn CH., Leergaard TB., Caleo M, Destexhe A, Ijspeert A, Micera S, Laschi C, Jirsa V, Gewaltig MO and Pavone FS. (2020) [Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience. ] Frontiers in Systems Neuroscience. 14:31 [url] [doi]
Jordan J, Helias M, Diesmann M and Kunkel S (2020) [Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions. ] Frontiers Media SA Frontiers in Neuroinformatics. 14:12 [url] [doi]
Senden M, Peters J, Röhrbein F, Deco G and Goebel R (2020) [Editorial: The Embodied Brain: Computational Mechanisms of Integrated Sensorimotor Interactions With a Dynamic Environment. ] Frontiers in Computational Neuroscience. 14:53 [url] [doi]
Crook SM., Davison AP., McDougal RA. and Plesser HE (2020) [Editorial: Reproducibility and Rigour in Computational Neuroscience. ] Frontiers in Neuroinformatics. 14:23 [url] [doi]
Kim CM, Egert U and Kumar A (2020) [Dynamics of multiple interacting excitatory and inhibitory populations with delays. ] Physical Review E. 102:022308 [url] [doi]
Senk J, Korvasova K, Schuecker J, Hagen E, Tetzlaff T, Diesmann M and Helias M (2020) [Conditions for wave trains in spiking neural networks. ] American Physical Society (APS) Physical Review Research. 2:023174 [url] [doi]
Kauth K, Stadtmann T, Brandhofer R, Sobhani V and Gemmeke T (2020) [Communication architecture enabling 100x accelerated simulation of biological neural networks. ], Proceedings of the Workshop on System-Level Interconnect: Problems and Pathfinding Workshop. :1-8 [url] [doi]
Vandesompele A, Urbain G, Wyffels F and Dambre J (2020) [Closed Loop Control of a Compliant Quadruped with Spiking Neural Networks. ]:547-555 [pdf] [doi]
Mitchell J. P, Schuman CD., Patton RM. and Potok TE. (2020) [Caspian. ], Proceedings of the Neuro-inspired Computational Elements Workshop. :1-6 [url] [doi]
Ahmed K (2020) [Brain-Inspired Spiking Neural Networks. ] [url] [doi]
Lee JH (2020) [Biologically plausible mechanisms underlying motor response correction during reward-based decision-making. ] Neurocomputing. 412:416-425 [url] [doi]
Kohler M, Stratmann P, Röhrbein F, Knoll A, Albu-Schäffer A and Jörntell H (2020) [Biological data questions the support of the self inhibition required for pattern generation in the half center model. ] PLoS ONE. 15:e0238586 [url] [doi]
Kulkarni SR., Parsa M, Mitchell J. P and Schuman CD. (2021) [Benchmarking the performance of neuromorphic and spiking neural network simulators. ] Neurocomputing. 447:145-160 [url] [doi]
Ostrau C, Klarhorst C, Thies M and Rückert U (2020) [Benchmarking of Neuromorphic Hardware Systems. ], Proceedings of the Neuro-inspired Computational Elements Workshop. :1-4 [url] [doi]
Ostrau C, Homburg J, Klarhorst C, Thies M and Rückert U (2020) [Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware. ]:610-621 [url] [doi]
Antolik J, Sabatier Q, Galle C, Frégnac Y and Benosman R (2021) [Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1. ] Scientific Reports. 11:10783 [pdf] [doi]
Fiebig F, Herman P and Lansner A (2020) [An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity. ] Society for Neuroscience eneuro. 7:ENEURO.0374–19.2020 [url] [doi]
Lazar AA, Liu T, Turkcan MK and Zhou Y (2021) [Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic era. ] eLife. 10:e62362 [url] [doi]
Abdoli B and Safari S (2020) [A reconfigurable real‐time neuromorphic hardware for spiking winner‐take‐all network. ] International Journal of Circuit Theory and Applications. 48:2141–2152 [url] [doi]
Huyck CR. (2020) [A neural cognitive architecture. ] Elsevier BV Cognitive Systems Research. 59:171–178 [url] [doi]
Müller MG, Papadimitriou CH, Maass W and Legenstein R (2020) [A model for structured information representation in neural networks of the brain. ] eNeuro. 7:eneuro.0533-19.2020 [pdf] [doi]
He H, Wang Q, Yang X, Lei Y, Cai J and Deng N (2021) [A memory neural system built based on spiking neural network. ] Neurocomputing. 442:146-160 [url] [doi]
Wang S, Wang L, Kang Z, Qu L, Li S and Su J (2020) [A Software-Hardware Co-exploration Framework for Optimizing Communication in Neuromorphic Processor. ]:87-100 [url] [doi]
Nadji-Tehrani M and Eslami A (2020) [A Brain-Inspired Framework for Evolutionary Artificial General Intelligence. ], 2020 Information Theory and Applications Workshop (ITA). :1-18 [url] [doi]
Kasabov NK. (2019) [Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence. ] Springer Berlin Heidelberg [url] [doi]
Gast R, Rose D, Salomon C, Möller HE., Weiskopf N and Knösche TR. (2019) [PyRates - A Python framework for rate-based neural simulations. ] Public Library of Science (PLoS) PLOS ONE. 14:e0225900 [url] [doi]
Zajzon B, Mahmoudian S, Morrison A and Duarte R (2019) [Passing the Message: Representation Transfer in Modular Balanced Networks. ] Frontiers Media SA Frontiers in Computational Neuroscience. 13:79 [url] [doi]
Igarashi J, Yamaura H and Yamazaki T (2019) [Large-Scale Simulation of a Layered Cortical Sheet of Spiking Network Model Using a Tile Partitioning Method. ] Frontiers Media SA Frontiers in Neuroinformatics. 13:71 [url] [doi]
Chen S, He Z, Han X, He X, Li R, Zhu H, Zhao D, Dai C, Zhang Y, Lu Z, Chi X and Niu B (2019) [How Big Data and High-performance Computing Drive Brain Science. ] Elsevier BV Genomics, Proteomics & Bioinformatics. 17:381–392 [url] [doi]
Crone JC., Vindiola MM., Yu AB., Boothe DL., Beeman D, Oie KS. and Franaszczuk PJ. (2019) [Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator. ] Frontiers Media SA Frontiers in Neuroinformatics. 13:69 [url] [doi]
Horton P, Huyck C and Wang X (2018) [Neural 2D Cart and Pole Control and Forward Model. ] Springer International Publishing:119–127 [url] [doi]
Jordan J, Petrovici MA., Breitwieser O, Schemmel J, Meier K, Diesmann M and Tetzlaff T (2019) [Deterministic networks for probabilistic computing. ] Springer Science and Business Media LLC Scientific Reports. 9:18303 [url] [doi]
Kobayashi R, Kurita S, Kurth A, Kitano K, Mizuseki K, Diesmann M, Richmond BJ. and Shinomoto S (2019) [Reconstructing neuronal circuitry from parallel spike trains. ] Springer Science and Business Media LLC Nature Communications. 10:4468 [url] [doi]
Machado P, Cosma G and McGinnity T. M (2019) [NatCSNN: A Convolutional Spiking Neural Network for Recognition of Objects Extracted from Natural Images. ] Springer International Publishing:351–362 [url] [doi]
Chrysanthidis N, Fiebig F and Lansner A (2019) [Introducing double bouquet cells into a modular cortical associative memory model. ] Springer Science and Business Media LLC Journal of Computational Neuroscience. 47:223–230 [url] [doi]
Baek E, Lee H, Kim Y and Kim J (2019) [FlexLearn. ], Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture - MICRO '52. ACM Press [url] [doi]
Kaiser J, Hoff M, Konle A, Tieck J. CV, Kappel D, Reichard D, Subramoney A, Legenstein R, Roennau A, Maass W and Dillmann R (2019) [Embodied Synaptic Plasticity With Online Reinforcement Learning. ] Frontiers Media SA Frontiers in Neurorobotics. 13:81 [url] [doi]
Morén J, Igarashi J, Shouno O, Yoshimoto J and Doya K (2019) [Dynamics of Basal Ganglia and Thalamus in Parkinsonian Tremor. ] Springer International Publishing:13–20 [url] [doi]
Kumbhar P, Hines M, Fouriaux J, Ovcharenko A, King J, Delalondre F and Schürmann F (2019) [CoreNEURON : An Optimized Compute Engine for the NEURON Simulator. ] Frontiers Media SA Frontiers in Neuroinformatics. 13:63 [url] [doi]
Oehrl S, Müller J, Schnathmeier J, Eppler JM, Peyser A, Plesser HE, Weyers B, Hentschel B, Kuhlen TW. and Vierjahn T (2018) [Streaming Live Neuronal Simulation Data into Visualization and Analysis. ] Springer International Publishing:258–272 [url] [doi]
Birgiolas J, Crook SM. and Gerkin RC. (2018) [Resources for Modeling in Computational Neuroscience. ] Springer International Publishing:807–830 [url] [doi]
Zielasko D, Zhao X, Demiralp AC, Kuhlen TW. and Weyers B (2019) [Voxel-based edge bundling through direction-aware kernel smoothing. ] Elsevier BV Computers & Graphics. 83:87–96 [url] [doi]
Yu Q, Li H and Tan KC (2019) [Spike Timing or Rate? Neurons Learn to Make Decisions for Both Through Threshold-Driven Plasticity. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Cybernetics. 49:2178–2189 [url] [doi]
Vandesompele A, Urbain G, wyffels F and Dambre J (2019) [Populations of spiking neurons for reservoir computing: Closed loop control of a compliant quadruped. ] Elsevier BV Cognitive Systems Research. 58:317–323 [url] [doi]
Talanov M, Leukhin A, Lövheim H, Vallverdú J, Toschev A and Gafarov F (2019) [Modeling Psycho-Emotional States via Neurosimulation of Monoamine Neurotransmitters. ] Springer International Publishing:127–156 [url] [doi]
Syeda F., Kumbhare D., Baron M. S. and Hadimani R. L. (2019) [Modeling of Transcranial Magnetic Stimulation Versus Pallidal Deep Brain Stimulation for Parkinson's Disease. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Magnetics:1–5 [url] [doi]
Stimberg M, Goodman DF. M., Brette R and Pittà MD (2019) [Modeling Neuron-Glia Interactions with the Brian 2 Simulator. ] Springer International Publishing:471–505 [url] [doi]
Stimberg M, Brette R and Goodman DF (2019) [Brian 2, an intuitive and efficient neural simulator. ] eLife Sciences Publications, Ltd eLife. 8:e47314 [url] [doi]
Stepniewski M., Breit M., Hoffer M. and Queisser G. (2019) [NeuroBox: computational mathematics in multiscale neuroscience. ] Springer Science and Business Media LLC Computing and Visualization in Science. 20:111–124 [url] [doi]
Simula F, Pastorelli E, Paolucci PS, Martinelli M, Lonardo A, Biagioni A, Capone C, Capuani F, Cretaro P, Bonis GD, Cicero FL, Pontisso L, Vicini P and Ammendola R (2019) [Real-Time Cortical Simulations: Energy and Interconnect Scaling on Distributed Systems. ], 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE [url] [doi]
Saunders DJ., Patel D, Hazan H, Siegelmann HT. and Kozma R (2019) [Locally connected spiking neural networks for unsupervised feature learning. ] Elsevier BV Neural Networks. 119:332–340 [url] [doi]
Nguyen QAP, Andelfinger P, Cai W and Knoll A (2019) [Transitioning Spiking Neural Network Simulators to Heterogeneous Hardware. ], Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation - SIGSIM-PADS '19. ACM Press [url] [doi]
Narasimhamurthy S, Danilov N, Wu S, Umanesan G, Markidis S, Rivas-Gomez S, Peng IB, Laure E, Pleiter D and Witt Sde (2019) [SAGE: Percipient Storage for Exascale Data Centric Computing. ] Elsevier BV Parallel Computing. 83:22–33 [url] [doi]
Mozafari M, Ganjtabesh M, Nowzari-Dalini A and Masquelier T (2019) [SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron. ] Frontiers Media SA Frontiers in Neuroscience. 13:625 [url] [doi]
Magalhaes BR. C., Sterling T, Hines M and Schürmann F (2019) [Asynchronous Branch-Parallel Simulation of Detailed Neuron Models. ] Frontiers Media SA Frontiers in Neuroinformatics. 13:54 [url] [doi]
Lagzi F, Atay FM. and Rotter S (2019) [Bifurcation analysis of the dynamics of interacting subnetworks of a spiking network. ] Springer Science and Business Media LLC Scientific Reports. 9:11397 [url] [doi]
Khoyratee F, Nishikawa SM, Zhongyue L, Kim SH, Saighi S, Fujii T, Ikeuchi Y, Aihara K and Levi T (2019) [Biomimetic Spiking Neural Network (SNN) Systems for `In Vitro' Cells Stimulation. ], 2019 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE [url] [doi]
Khoyratee F, Grassia F, Saïghi S and Levi T (2019) [Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization. ] Frontiers Media SA Frontiers in Neuroscience. 13:377 [url] [doi]
Gleeson P, Cantarelli M, Marin B, Quintana A, Earnshaw M, Sadeh S, Piasini E, Birgiolas J, Cannon RC., Cayco-Gajic N. A, Crook S, Davison AP., Dura-Bernal S, Ecker A, Hines ML., Idili G, Lanore F, Larson SD., Lytton WW., Majumdar A, McDougal RA., Sivagnanam S, Solinas S, Stanislovas R, Albada SJ. van, Geit Wvan and Silver R. A (2019) [Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits. ] Elsevier BV Neuron. 103:395–411.e5 [url] [doi]
Dold D, Bytschok I, Kungl AF., Baumbach A, Breitwieser O, Senn W, Schemmel J, Meier K and Petrovici MA. (2019) [Stochasticity from function - Why the Bayesian brain may need no noise. ] Elsevier BV Neural Networks. 119:200–213 [url] [doi]
Caligiore D, Mannella F and Baldassarre G (2019) [Different Dopaminergic Dysfunctions Underlying Parkinsonian Akinesia and Tremor. ] Frontiers Media SA Frontiers in Neuroscience. 13:550 [url] [doi]
Bornet A, Kaiser J, Kroner A, Falotico E, Ambrosano A, Cantero K, Herzog MH. and Francis G (2019) [Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision - The Case of Visual Crowding. ] Frontiers Media SA Frontiers in Neurorobotics. 13:33 [url] [doi]
Bohnstingl T, Scherr F, Pehle C, Meier K and Maass W (2019) [Neuromorphic Hardware Learns to Learn. ] Frontiers Media SA Frontiers in Neuroscience. 13:483 [url] [doi]
Bisio M, Pimashkin A, Buccelli S, Tessadori J, Semprini M, Levi T, Colombi I, Gladkov A, Mukhina I, Averna A, Kazantsev V, Pasquale V and Chiappalone M (2019) [Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications. ] Springer International Publishing:351–387 [url] [doi]
Bing Z, Jiang Z, Cheng L, Cai C, Huang K and Knoll A (2019) [End to End Learning of a Multi-Layered Snn Based on R-Stdp for a Target Tracking Snake-Like Robot. ], 2019 International Conference on Robotics and Automation (ICRA). IEEE [url] [doi]
Andalibi V, Hokkanen H and Vanni S (2019) [Controlling Complexity of Cerebral Cortex Simulations - I: CxSystem, a Flexible Cortical Simulation Framework. ] MIT Press - Journals Neural Computation. 31:1048–1065 [url] [doi]
Jordan J, Weidel P and Morrison A (2019) [A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents. ] Frontiers Media SA Frontiers in Computational Neuroscience. 13:46 [url] [doi]
Merkt B, Schüßler F and Rotter S (2019) [Propagation of orientation selectivity in a spiking network model of layered primary visual cortex. ] Public Library of Science (PLOS) PLOS Computational Biology. 15:e1007080 [url] [doi]
Dahmen D, Grün S, Diesmann M and Helias M (2019) [Second type of criticality in the brain uncovers rich multiple-neuron dynamics. ] Proceedings of the National Academy of Sciences Proceedings of the National Academy of Sciences. 116:13051–13060 [url] [doi]
Rasmussen D (2019) [NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods. ] Springer Nature Neuroinformatics. 17:611–628 [url] [doi]
Fernandez-Musoles C, Coca D and Richmond P (2019) [Communication Sparsity in Distributed Spiking Neural Network Simulations to Improve Scalability. ] Frontiers Media SA Frontiers in Neuroinformatics. 13:19 [url] [doi]
Jokar E, Abolfathi H and Ahmadi A (2019) [A Novel Nonlinear Function Evaluation Approach for Efficient FPGA Mapping of Neuron and Synaptic Plasticity Models. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Biomedical Circuits and Systems. 13:454–469 [url] [doi]
Chen G, Bing Z, Rohrbein F, Conradt J, Huang K, Cheng L, Jiang Z and Knoll A (2019) [Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform. ] Institute of Electrical and Electronics Engineers (IEEE) IEEE Transactions on Cognitive and Developmental Systems. 11:1–12 [url] [doi]
Haessig G, Berthelon X, Ieng SH and Benosman R (2019) [A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision. ] Springer Nature Scientific Reports. 9:3744 [url] [doi]
Iyer R, Menon V, Buice M, Koch C and Mihalas S (2013) [The Influence of Synaptic Weight Distribution on Neuronal Population Dynamics. ] Public Library of Science (PLoS) PLoS Computational Biology. 9:e1003248 [url] [doi]
Rhodes O, Bogdan PA., Brenninkmeijer C, Davidson S, Fellows D, Gait A, Lester DR., Mikaitis M, Plana LA., Rowley AG. D., Stokes AB. and Furber SB. (2018) [sPyNNaker: A Software Package for Running PyNNSimulations on SpiNNaker. ] Frontiers Media SA Frontiers in Neuroscience. 12:816 [url] [doi]
Martinez-Canada P, Mobarhan MH, Halnes G, Fyhn M, Morillas C, Pelayo F and Einevoll GT. (2018) [Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells. ] Public Library of Science (PLOS) PLOS Computational Biology. 14:e1005930 [url] [doi]
Talanov M, Gafarov F, Vallverdú J, Ostapenko S, Gazizov M, Toschev A, Leukhin A and Distefano S (2018) [Simulation of serotonin mechanisms in NEUCOGAR cognitive architecture. ] Elsevier BV Procedia Computer Science. 123:473–478 [url] [doi]
Sherfey JS., Soplata AE., Ardid S, Roberts EA., Stanley DA., Pittman-Polletta BR. and Kopell NJ. (2018) [DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:10 [url] [doi]
Kappel D, Legenstein R, Habenschuss S, Hsieh M and Maass W (2018) [A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning. ] Society for Neuroscience eneuro. 5:ENEURO.0301–17.2018 [url] [doi]
Wang RM., Thakur CS. and Schaik Avan (2018) [An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator. ] Frontiers Media SA Frontiers in Neuroscience. 12:213 [url] [doi]
Mulugeta L, Drach A, Erdemir A, Hunt C. A., Horner M, Ku JP., Jr. JG. M, Vadigepalli R and Lytton WW. (2018) [Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:18 [url] [doi]
Mostafa H and Cauwenberghs G (2018) [A Learning Framework for Winner-Take-All Networks with Stochastic Synapses. ] MIT Press - Journals Neural Computation. 30:1542–1572 [url] [doi]
Casadiego J, Maoutsa D and Timme M (2018) [Inferring Network Connectivity from Event Timing Patterns. ] American Physical Society (APS) Physical Review Letters. 121:054101 [url] [doi]
Sanz-Leon P, Robinson PA., Knock SA., Drysdale PM., Abeysuriya RG., Fung FK., Rennie CJ. and Zhao X (2018) [NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics. ] Public Library of Science (PLoS) PLOS Computational Biology. 14:e1006387 [url] [doi]
Rinke S, Butz-Ostendorf M, Hermanns MA, Naveau M and Wolf F (2018) [A scalable algorithm for simulating the structural plasticity of the brain. ] Elsevier BV Journal of Parallel and Distributed Computing. 120:251–266 [url] [doi]
Akbarzadeh-Sherbaf K, Abdoli B, Safari S and Vahabie AH (2018) [A Scalable FPGA Architecture for Randomly Connected Networks of Hodgkin-Huxley Neurons. ] Frontiers Media SA Frontiers in Neuroscience. 12:698 [url] [doi]
Miska NJ, Richter LM, Cary BA, Gjorgjieva J and Turrigiano GG (2018) [Sensory experience inversely regulates feedforward and feedback excitation-inhibition ratio in rodent visual cortex. ] eLife Sciences Publications, Ltd eLife. 7:e38846 [url] [doi]
Scheffer LK. (2018) [Analysis Tools for Large Connectomes. ] Frontiers Media SA Frontiers in Neural Circuits. 12:85 [url] [doi]
Huyck C and Mitchell I (2018) [CABots and Other Neural Agents. ] Frontiers Media SA Frontiers in Neurorobotics. 12:79 [url] [doi]
Hazan H, Saunders DJ., Khan H, Patel D, Sanghavi DT., Siegelmann HT. and Kozma R (2018) [BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:3389 [url] [doi]
Gajewska-Dendek E, Wróbel A, Bekisz M and Suffczynski P (2018) [Lateral Inhibition Organizes Beta Attentional Modulation in the Primary Visual Cortex. ] World Scientific Pub Co Pte Lt International Journal of Neural Systems:1850047 [url] [doi]
Popovych OV., Manos T, Hoffstaedter F and Eickhoff SB. (2019) [What Can Computational Models Contribute to Neuroimaging Data Analytics?. ] Frontiers Media SA Frontiers in Systems Neuroscience. 12:68 [url] [doi]
Antonietti A, Martina D, Casellato C, D'Angelo E and Pedrocchi A (2019) [Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks. ] Hindawi Limited Computational Intelligence and Neuroscience. 2019:1–15 [url] [doi]
Nowke C, Diaz-Pier S, Weyers B, Hentschel B, Morrison A, Kuhlen TW. and Peyser A (2018) [Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:32 [url] [doi]
Fardet T, Bottani S, Métens S and Monceau P (2018) [Effects of inhibitory neurons on the quorum percolation model and dynamical extension with the Brette–Gerstner model. ] Physica A: Statistical Mechanics and its Applications. 499:98 - 109 [url] [doi]
Geminiani A, Casellato C, Locatelli F, Prestori F, Pedrocchi A and D'Angelo E (2018) [Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness. ] Frontiers in Neuroinformatics. 12:88 [url] [doi]
Bing Z, Meschede C, Huang K, Chen G, Rohrbein F, Akl M and Knoll A (2018) [End to End Learning of Spiking Neural Network Based on R-STDP for a Lane Keeping Vehicle. ], 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE [url] [doi]
Kaiser J, Weinland J, Keller P, Steffen L, Tieck J. CV, Reichard D, Roennau A, Conradt J and Dillmann R (2018) Microsaccades for Neuromorphic Stereo Vision. In Krurková, Vvera and Manolopoulos, Yannis and Hammer, Barbara and Iliadis, Lazaros and Maglogiannis, Ilias, Artificial Neural Networks and Machine Learning – ICANN 2018. Springer International Publishing:244–252
Czauderna T, Haga J, Kim J, Klapperstück M, Klein K, Kuhlen T, Oeltze-Jafra S, Sommer B and Schreiber F (2018) [Immersive Analytics Applications in Life and Health Sciences. ] Springer International Publishing:289–330 [url] [doi]
Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M and Albada SJvan (2018) [A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. ] Public Library of Science PLoS PLOS Computational Biology. 14:e1006359 [url] [doi]
Nayak L, Dasgupta A, Das R, Ghosh K and De RK (2018) [Computational neuroscience and neuroinformatics: Recent progress and resources. ] Springer Nature America, Inc Journal of Biosciences. 43:1037–1054 [url] [doi]
Leukhin A, Talanov M, Vallverdú J and Gafarov F (2018) [Bio-plausible simulation of three monoamine systems to replicate emotional phenomena in a machine. ] Elsevier BV Biologically Inspired Cognitive Architectures. 26:166–173 [url] [doi]
Knight JC. and Nowotny T (2018) [GPUs Outperform Current HPC and Neuromorphic Solutions in Terms of Speed and Energy When Simulating a Highly-Connected Cortical Model. ] Frontiers Media SA Frontiers in Neuroscience. 12:941 [url] [doi]
van Meegen A and Lindner B (2018) [Self-Consistent Correlations of Randomly Coupled Rotators in the Asynchronous State. ] American Physical Society Phys. Rev. Lett.. 121:258302 [url] [doi]
Mapar F and Weiss R (2018) [Design and Analysis of Staged Mutual Inhibition to Implement Bi-Stable Neuronal Toggle Switch. ], 2018 IEEE Biomedical Circuits and Systems Conference BioCAS. IEEE [url] [doi]
Trensch G, Gutzen R, Blundell I, Denker M and Morrison A (2018) [Rigorous Neural Network Simulations: A Model Substantiation Methodology for Increasing the Correctness of Simulation Results in the Absence of Experimental Validation Data. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:3389 [url] [doi]
Gutzen R, Papen Mvon, Trensch G, Quaglio P, Grün S and Denker M (2018) [Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:90 [url] [doi]
Tennøe S, Halnes G and Einevoll GT. (2018) [Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:49 [url] [doi]
Blundell I, Plotnikov D, Eppler JM. and Morrison A (2018) [Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:50 [url] [doi]
Maksimov A, Diesmann M and Albada SJ. van (2018) [Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models. ] Frontiers Media SA Frontiers in Computational Neuroscience. 12:44 [url] [doi]
Bahuguna J, Weidel P and Morrison A (2018) [Exploring the role of striatal D1 and D2 medium spiny neurons in action selection using a virtual robotic framework. ] Wiley European Journal of Neuroscience. 49:737–753 [url] [doi]
Pauli R, Weidel P, Kunkel S and Morrison A (2018) [Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:46 [url] [doi]
Heiberg T, Kriener B, Tetzlaff T, Einevoll GT. and Plesser HE. (2018) [Firing-rate models for neurons with a broad repertoire of spiking behaviors. ] Springer Nature America, Inc Journal of Computational Neuroscience. 45:103–132 [url] [doi]
Blundell I, Brette R, Cleland TA., Close TG., Coca D, Davison AP., Diaz-Pier S, Fernandez Musoles C, Gleeson P, Goodman DF. M., Hines M, Hopkins MW., Kumbhar P, Lester DR., Marin B, Morrison A, Müller E, Nowotny T, Peyser A, Plotnikov D, Richmond P, Rowley A, Rumpe B, Stimberg M, Stokes AB., Tomkins A, Trensch G, Woodman M and Eppler JM (2018) [Code Generation in Computational Neuroscience: A Review of Tools and Techniques. ] Frontiers in Neuroinformatics. 12:68 [url] [doi]
Senk J, Carde C, Hagen E, Kuhlen TW., Diesmann M and Weyers B (2018) [VIOLA - A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output. ] Frontiers in Neuroinformatics. 12:75 [url] [doi]
Singh N, Huyck C, Gandhi V and Jones A (2017) [Neuron-Based Control Mechanisms for a Robotic Arm and Hand. ] [url] [doi]
Setareh H, Deger M and Gerstner W (2018) [Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation. ] Public Library of Science (PLoS) PLOS Computational Biology. 14:e1006216 [url] [doi]
Sengupta N, Ramos JIE, Tu E, Marks S, Scott N, Weclawski J, Gollahalli AR, Doborjeh MG, Doborjeh ZG, Kumarasinghe K, Breen V and Abbott A (2018) [From von Neumann Architecture and Atanasoffs ABC to Neuro-Morphic Computation and Kasabov's NeuCube: Principles and Implementations. ], Studies in Computational Intelligence. Springer International Publishing:1–28 [url] [doi]
Planas J, Delalondre F and Schürmann F (2018) [Accelerating Data Analysis in Simulation Neuroscience with Big Data Technologies. ], Lecture Notes in Computer Science. Springer International Publishing:363–377 [url] [doi]
Pastorelli E, Paolucci PS, Simula F, Biagioni A, Capuani F, Cretaro P, Bonis GD, Cicero FL, Lonardo A, Martinelli M, Pontisso L, Vicini P and Ammendola R (2018) [Gaussian and Exponential Lateral Connectivity on Distributed Spiking Neural Network Simulation. ], 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE [url] [doi]
Nair M, Kannimoola JM, Jayaraman B, Nair B and Diwakar S (2018) [Temporal constrained objects for modelling neuronal dynamics. ] PeerJ PeerJ Computer Science. 4:e159 [url] [doi]
Long Y, She X and Mukhopadhyay S (2018) [Accelerating biophysical neural network simulation with region of interest based approximation. ], 2018 Design, Automation & Test in Europe Conference &vExhibition (DATE). IEEE [url] [doi]
Lee D, Lee G, Kwon D, Lee S, Kim Y and Kim J (2018) [Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations. ], 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA). IEEE [url] [doi]
Kautz O, Roth A and Rumpe B (2018) [Achievements, Failures, and the Future of Model-Based Software Engineering. ] Springer International Publishing:221–236 [url] [doi]
Antolík J and Davison AP. (2018) [Arkheia: Data Management and Communication for Open Computational Neuroscience. ] Frontiers Media SA Frontiers in Neuroinformatics. 12:6 [url] [doi]
Martínez-Cañada P, Morillas C, Nieves JL, Pino B and Pelayo F (2015) First Stage of a Human Visual System Simulator: The Retina. In Trémeau, Alain and Schettini, Raimondo and Tominaga, Shoji, Computational Color Imaging. Springer International Publishing:118–127
Lagzi F and Rotter S (2015) [Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State. ] Public Library of Science PLOS ONE. 10:1-28 [url] [doi]
Erçelik E., Elibol R. and Şengör N. S. (2015) A model on building and modifying the stimulus action association in the brain. , 2015 23nd Signal Processing and Communications Applications Conference (SIU). :2533-2536 [doi]
Pozzorini C, Mensi S, Hagens O, Naud R, Koch C and Gerstner W (2015) [Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models. ] Public Library of Science PLOS Computational Biology. 11:1-29 [url] [doi]
Tomsett RJ., Ainsworth M, Thiele A, Sanayei M, Chen X, Gieselmann MA., Whittington MA., Cunningham MO. and Kaiser M (2015) [Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue. ] Brain Structure and Function. 220:2333–2353 [url] [doi]
Jahnke S, Timme M and Memmesheimer RM (2015) [A Unified Dynamic Model for Learning, Replay, and Sharp-Wave/Ripples. ] Society for Neuroscience Journal of Neuroscience. 35:16236–16258 [url] [doi]
Zaytsev YV., Morrison A and Deger M (2015) [Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity. ] Journal of Computational Neuroscience. 39:77–103 [url] [doi]
Dahmen D, Bos H and Helias M (2016) [Correlated Fluctuations in Strongly Coupled Binary Networks Beyond Equilibrium. ] American Physical Society Phys. Rev. X. 6:031024 [url] [doi]
Sreenivasa M., Ayusawa K. and Nakamura Y. (2016) Modeling and Identification of a Realistic Spiking Neural Network and Musculoskeletal Model of the Human Arm, and an Application to the Stretch Reflex. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 24:591-602 [doi]
Knight JC., Tully PJ., Kaplan BA., Lansner A and Furber SB. (2016) [Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware. ] Frontiers in Neuroanatomy. 10:37 [url] [doi]
Tully PJ., Lindén H, Hennig MH. and Lansner A (2016) [Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. ] Public Library of Science PLOS Computational Biology. 12:1-35 [url] [doi]
Ahmed K., Shrestha A. and Qiu Q (2016) Simulation of bayesian learning and inference on distributed stochastic spiking neural networks. , 2016 International Joint Conference on Neural Networks (IJCNN). :1044-1051 [doi]
Berthet P, Lindahl M, Tully PJ., Hellgren-Kotaleski J and Lansner A (2016) [Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity. ] Frontiers in Neural Circuits. 10:53 [url] [doi]
Lindahl M and Hellgren Kotaleski J (2016) [Untangling Basal Ganglia Network Dynamics and Function: Role of Dopamine Depletion and Inhibition Investigated in a Spiking Network Model. ] Society for Neuroscience eNeuro. 3 [url] [doi]
Sboev A, Vlasov D, Serenko A, Rybka R and Moloshnikov I (2016) [A comparison of learning abilities of spiking networks with different spike timing-dependent plasticity forms. ] Journal of Physics: Conference Series. 681:012013 [url] [doi]
Cattani A, Solinas S and Canuto C (2016) [A Hybrid Model for the Computationally-Efficient Simulation of the Cerebellar Granular Layer. ] Frontiers in Computational Neuroscience. 10:30 [url] [doi]
Sboev A., Vlasov D., Serenko A., Rybka R. and Moloshnikov I. (2016) [On the applicability of STDP-based learning mechanisms to spiking neuron network models. ] AIP Advances. 6:111305 [url] [doi]
Schuecker J, Schmidt M, van Albada SJ., Diesmann M and Helias M (2017) [Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. ] Public Library of Science PLOS Computational Biology. 13:1-25 [url] [doi]
Fiebig F and Lansner A (2017) [A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation. ] Society for Neuroscience Journal of Neuroscience. 37:83–96 [url] [doi]
Lee JH and Mihalas S (2017) [Visual processing mode switching regulated by VIP cells. ] Scientific Reports. 7:1843 [url] [doi]
Krishnan J, Porta Mana P, Helias M, Diesmann M and Di Napoli E (2018) [Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons. ] Frontiers in Neuroinformatics. 11:75 [url] [doi]
Aasebo IE. J., Lepperod ME., Stavrinou M, Nokkevangen S, Einevoll G, Hafting T and Fyhn M (2017) [Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat. ] Society for Neuroscience eNeuro. 4 [url] [doi]
Deniz T and Rotter S (2017) [Joint statistics of strongly correlated neurons via dimensionality reduction. ] Journal of Physics A: Mathematical and Theoretical. 50:254002 [url] [doi]
Mirzaei A, Kumar A, Leventhal D, Mallet N, Aertsen A, Berke J and Schmidt R (2017) [Sensorimotor Processing in the Basal Ganglia Leads to Transient Beta Oscillations during Behavior. ] Society for Neuroscience Journal of Neuroscience. 37:11220–11232 [url] [doi]
Sadeh S, Silver R. A, Mrsic-Flogel TD. and Muir DR (2017) [Assessing the Role of Inhibition in Stabilizing Neocortical Networks Requires Large-Scale Perturbation of the Inhibitory Population. ] Society for Neuroscience Journal of Neuroscience. 37:12050–12067 [url] [doi]
Selyunin K, Hasani RM., Ratasich D, Bartocci E and Grosu R (2017) Computing with Biophysical and Hardware-Efficient Neural Models. In Rojas, Ignacio and Joya, Gonzalo and Catala, Andreu, Advances in Computational Intelligence. Springer International Publishing:535–547
van Albada SJ., Rowley AG., Senk J, Hopkins M, Schmidt M, Stokes AB., Lester DR., Diesmann M and Furber SB. (2018) [Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model. ] Frontiers in Neuroscience. 12:291 [url] [doi]
Gallinaro JV. and Rotter S (2018) [Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks. ] Scientific Reports. 8:3754 [url] [doi]
Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M and Kunkel S (2018) [Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. ] Frontiers in Neuroinformatics. 12:2 [url] [doi]
Zaytsev YV. and Morrison A (2013) Increasing quality and managing complexity in neuroinformatics software development with continuous integration. Front. Neuroinform.. 6:31 [file] [doi]
Zaytsev YV. and Morrison A (2014) CyNEST: a maintainable Cython-based interface for the NEST simulator. Frontiers in Neuroinformatics. 8:23 [doi]
Yuan CW and Leibold C (2012) Recurrent coupling improves discrimination of temporal spike patterns. Front Comput Neurosci. 6:25 [doi]
Yim M.Y., Aertsen A. and Kumar A. (2011) Significance of input correlations in striatal function. PLoS Comput Biol. 7:e1002254 [doi]
Yger P and Harris KD. (2013) [The Convallis Rule for Unsupervised Learning in Cortical Networks. ] PLOS Comput Bio. 9:10 [url] [doi]
Yger P, Boustani S, Destexhe A and Frégnac Y (2011) Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons. Springer US Journal of Computational Neuroscience. 31:229–245 [doi]
Yen Yyang, Chen Lray, Huang Yzu and Lo CC (2012) Models of cortico-basal ganglia circuits and synaptic plasticity for transcranial magnetic stimulation. , Complex Medical Engineering (CME), 2012 ICME International Conference on. :96–100 [doi]
Yamazaki T, Ikeno H, Okumura Y, Satoh S, Kamiyama Y, Hirata Y, Inagaki K, Ishihara A, Kannon T and Usui S (2011) Simulation Platform: a cloud-based online simulation environment. Neural Netw. 24:693–698 [doi]
Yamauchi S., Kim H. and Shinomoto S. (2011) [Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times. ] Front Comput Neurosci. 5:42 [url] [doi]
Wildie M., Luk W., Schultz S.R., Leong P.H.-W. and Fidjeland A.K. (2009) Reconfigurable acceleration of neural models with gap junctions. , Field-Programmable Technology, 2009. FPT 2009. International Conference on. :439–442 [doi]
Westoe J and May PJ.C. (2016) [Capturing contextual effects in spectro-temporal receptive fields. ] Hearing Research. 339:195 - 210 [url] [doi]
Weidel P, Djurfeldt M, Duarte RC. and Morrison A (2016) Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS. Frontiers in Neuroinformatics. 10:31 [doi]
Wazny M and Wojcik GM. (2014) [Shifting spatial attention—Numerical model of Posner experiment. ] Neurocomputing. 135:139–144 [url] [doi]
Wang Z, Guo L and Adjouadi M (2016) Wavelet decomposition and phase encoding of temporal signals using spiking neurons. NEUROCOMPUTING. 173:1203–1210 [doi]
Wagatsuma N, Potjans TC., Diesmann M, Sakai K and Fukai T (2013) [Spatial and Feature-Based Attention in a Layered Cortical Microcircuit Model. ] PLoS ONE. 8:12 [url] [doi]
Wagatsuma N, Potjans TC, Diesmann M and Fukai T (2011) Layer-Dependent Attentional Processing by Top-down Signals in a Visual Cortical Microcircuit Model. Front Comput Neurosci. 5:31 [doi]
Waddington A, Appleby PA, Kamps MD and Cohen N (2012) Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity. Front Comput Neurosci. 6:88 [doi]
Voges N and Perrinet L (2010) Phase space analysis of networks based on biologically realistic parameters. J Physiol Paris. 104:51–60 [doi]
Vlachos I, Zaytsev YV., Spreizer S, Aertsen A and Kumar A (2013) Neural system prediction and identification challenge. Front Neuroinform.. 7:43 [doi]
Vlachos I, Herry C, Lüthi A, Aertsen A and Kumar A (2011) Context-dependent encoding of fear and extinction memories in a large-scale network model of the basal amygdala. PLoS Comput Biol. 7:e1001104 [doi]
Vlachos A, Helias M, Becker D, Diesmann M and Deller T (2013) [NMDA-receptor inhibition increases spine stability of denervated mouse dentate granule cells and accelerates spine density recovery following entorhinal denervation in vitro. ] Neurobiology of Disease. 59:267–276 [url] [doi]
Vitay J, Dinkelbach HÜ and Hamker FH (2015) [ANNarchy: a code generation approach to neural simulations on parallel hardware. ] Frontiers in Neuroinformatics. 9:19 [url] [doi]
Versace M, Ames H, Léveillé J, Fortenberry B and Gorchetchnikov A (2008) KInNeSS: a modular framework for computational neuroscience. Neuroinformatics. 6:291–309 [doi]
Veredas F.J., Vico F.J. and Alonso J.M. (2004) [A computational tool to simulate correlated activity in neural circuits. ] Journal of Neuroscience Methods. 136:23–32 [pdf] [doi]
Vella M., Cannon R., Crook S., Davison A., Ganapathy G., Robinson H. P. C., Silver A. and Gleeson P. (2014) [libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience. ] Front Neuroinform. 8:38 [url] [doi]
Vannucci L, Falotico E and Laschi C (2017) [Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model. ] Frontiers in Neuroscience. 11:341 [url] [doi]
Vanni S, Sharifian F, Heikkinen H and Vigário R (2015) [Modeling fMRI signals can provide insights into neural processing in the cerebral cortex. ] American Physiological Society Journal of Neurophysiology. 114:768–780 [url] [doi]
Vallverdu J, Talanov M, Distefano S, Mazzara M, Tchitchigin A and Nurgaliev I (2015) [A cognitive architecture for the implementation of emotions in computing systems. ] Biologically Inspired Cognitive Architectures:- [url] [doi]
Tsujino H, Takeuchi J and Shouno O (2009) Basal Ganglia Models for Autonomous Behavior Learning. Springer Berlin Heidelberg. 5436:328–350 [doi]
Trengove C, Leeuwen Cvan and Diesmann M (2012) High-capacity embedding of synfire chains in a cortical network model. J Comput Neurosci. 34:185–209 [doi]
Trengove C, Diesmann M and Leeuwen Cvan (2016) Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains. Journal of Computational Neuroscience. 40:1–26 [doi]
Touboul J (2011) On the Simulation of Nonlinear Bidimensional Spiking Neuron Models. Neural Comput. 23:1704–1742 [doi]
Tetzlaff T, Rotter S, Stark E, Abeles M, Aertsen A and Diesmann M (2008) Dependence of neuronal correlations on filter characteristics and marginal spike train statistics. Neural Comput. 20:2133–2184 [doi]
Tetzlaff T, Helias M, Einevoll GT and Diesmann M (2012) Decorrelation of neural-network activity by inhibitory feedback. PLoS Comput Biol. 8:e1002596 [doi]
Tetzlaff T., Buschermöhle M., Geisel T. and Diesmann M. (2003) [The spread of rate and correlation in stationary cortical networks. ], Neurocomputing. Neurocomputing 52–54 : 949-954 [url] [doi]
Tang Y, Zhang B, Wu J, Hu T, Zhou J and Liu F (2013) [Parallel architecture and optimization for discrete-event simulation of spike neural networks. ] SP Science China Press Science China Technological Sciences. 56:509–517 [url] [doi]
Talanov M, Vallverda J, Hu B, Moore P, Toschev A, Shatunova D, Maganova A, Sedlenko D and Leukhin A (2016) [Emotional simulations and depression diagnostics. ] Biologically Inspired Cognitive Architectures. 18:41 - 50 [url] [doi]
Sun H, Sourina O and Huang GB (2016) Learning Polychronous Neuronal Groups Using Joint Weight-Delay Spike-Timing-Dependent Plasticity. MIT Press Neural Computation. 28:2181–2212 [doi]
Sukhinin DI., Engel AK., Manger P and Hilgetag CC. (2016) Building the Ferretome. Frontiers in Neuroinformatics. 10:16 [doi]
Stöckel A, Jenzen C, Thies M and Rückert U (2017) [Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware. ] Frontiers in Computational Neuroscience. 11:71 [url] [doi]
Stimberg M., Goodman D., Benichoux V. and Brette1 R. (2014) [Equation-oriented specification of neural models for simulations. ] Front Neuroinform. 8:6 [url] [doi]
Stevens JLR., Elver M and Bednar JA. (2013) An automated and reproducible workflow for running and analyzing neural simulations using Lancet and IPython Notebook. Front Neuroinform. 7:44 [doi]
Stetter O, Battaglia D, Soriano J and Geisel T (2012) Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals. PLoS Comput Biol. 8:e1002653 [doi]
Sreenivasa M., Murai A. and Nakamura Y. (2013) [Modeling and identification of the human arm stretch reflex using a realistic spiking neural network and musculoskeletal model. ]:329–334 [url] [doi]
Spreizer S, Angelhuber M, Bahuguna J, Aertsen A and Kumar A (2017) [Activity Dynamics and Signal Representation in a Striatal Network Model with Distance-Dependent Connectivity. ] Society for Neuroscience eNeuro. 4:ENEURO.0348–16.2017 [url] [doi]
Sousa M and Aguiar P (2014) Building, simulating and visualizing large spiking neural networks with NeuralSyns. Neurocomputing. 123:280–372 [doi]
Solanka L, van Rossum MC and Nolan MF (2015) Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks. eLife Sciences Publications Limited eLife. 4:e06444 [doi]
Simonov A.Y. and Kazantsev V.B. (2011) Model of the appearance of avalanche bioelectric discharges in neural networks of the brain. SP MAIK Nauka/Interperiodica JETP Letters. 93:470–475 [doi]
Shouno O, Takeuchi J and Tsujino H (2009) A Spiking Neuron Model of the Basal Ganglia Circuitry that Can Generate Behavioral Variability. Springer New York. 58:191–200 [doi]
Sharp T and Furber S (2013) [Correctness and performance of the SpiNNaker architecture. ], Neural Networks (IJCNN), The 2013 International Joint Conference on. :1–8 [url] [doi]
Segundo J.Guzman ASCSH (2014) [Stimfit: quantifying electrophysiological data with Python. ] Front Neuroinform. 8:16 [url] [doi]
Segev A, Curtis D, Jung S and Chae S (2016) [Invisible Brain: Knowledge in Research Works and Neuron Activity. ] Public Library of Science PLoS ONE. 11:1-21 [url] [doi]
Schwartz MO (2013) [Reproducing Biologically Realistic Regimes on a Highly-Accelerated Neuromorphic Hardware System. ] [url] [doi]
Schwalger T, Deger M and Gerstner W (2017) [Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size. ] Public Library of Science PLOS Computational Biology. 13:1-63 [url] [doi]
Schutter ED (2008) Why are computational neuroscience and systems biology so separate?. PLoS Comput Biol. 4:e1000078 [doi]
Schutter E (2013) Collaborative Modeling in Neuroscience: Time to Go Open Model?. Springer-Verlag Neuroinformatics. 11:135–136 [doi]
Schultze-Kraft M, Diesmann M, Sonja Grün and Helias M (2011) Input synchrony strengthens correlation transmission via noise suppression. [doi]
Schultze-Kraft M, Diesmann M, Grün S and Helias M (2013) [Noise suppression and surplus synchrony by coincidence detection. ] Public Library of Science PLoS Comput Biol. 9:e1002904 [url] [doi]
Schuecker J, Diesmann M and Helias M (2015) Modulated escape from a metastable state driven by colored noise. American Physical Society Phys. Rev. E. 92:052119 [file] [doi]
Schrader S, Grün S, Diesmann M and Gerstein GL (2008) Detecting synfire chain activity using massively parallel spike train recording. J Neurophysiol. 100:2165–2176 [doi]
Schrader S, Gewaltig MO, Körner U and Körner E (2009) Cortext: a columnar model of bottom-up and top-down processing in the neocortex. Neural Netw. 22:1055–1070 [doi]
Schrader S, Diesmann M and Morrison A (2011) A compositionality machine realized by a hierarchic architecture of synfire chains. Front Comput Neurosci. 4:154 [doi]
Schmitt O, Eipert P, Kettlitz R, Leßmann F and Wree A (2016) The connectome of the basal ganglia. Brain Structure and Function. 221:753–814 [doi]
Schmitt O and Eipert P (2012) neuroVIISAS: approaching multiscale simulation of the rat connectome. Neuroinformatics. 10:243–267 [doi]
Schenck W, El Sayed S, Foszczynski M, Homberg W and Pleiter D (2017) [Evaluation and Performance Modeling of a Burst Buffer Solution. ] ACM SIGOPS Oper. Syst. Rev.. 50:12–26 [url] [doi]
Sboev A, Vlasov D, Serenko A, Rybka R and Moloshnikov I (2016) [To the Question of Learnability of a Spiking Neuron with Spike-Timing-Dependent Plasticity in Case of Complex Input Signals. ] Springer International Publishing:205–211 [url] [doi]
Salles Chevitarese D., Szwarcman D. and Vellasco M. (2015) Introduction to CircuitML: Modeling Local Processing Units in the drosophila brain. , Neural Networks (IJCNN), 2015 International Joint Conference on. :1–8 [doi]
Sahasranamam A, Vlachos I, Aertsen A and Kumar A (2016) Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity. The Author(s) Scientific Reports. 6:26029– [doi]
Sadeh S and Rotter S (2015) [Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics. ] Public Library of Science PLoS Comput Biol. 11:e1004045 [url] [doi]
Rougier NP. and Fix J (2012) DANA: distributed numerical and adaptive modelling framework. Network. 23:237–253 [doi]
Rothkegel A and Lehnertz K (2012) [Conedy: A scientific tool to investigate complex network dynamics. ] AIP Publishing Chaos: An Interdisciplinary Journal of Nonlinear Science. 22:013125 [url] [doi]
Rothganger F, Warrender CE., Trumbo D and Aimone JB. (2014) [N2A: a computational tool for modeling from neurons to algorithms. ] Front Neural Circuits. 8:1 [url] [doi]
Rostami V, Porta Mana P, Gruen S and Helias M (2017) [Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models. ] Public Library of Science PLOS Computational Biology. 13:1-44 [url] [doi]
Richmond P, Cope A, Gurney K and Allerton DJ. (2013) [From Model Specification to Simulation of Biologically Constrained Networks of Spiking Neurons. ] Neuroinformatics. 12:307–323 [url] [doi]
Richmond P, Buesing L, Giugliano M and Vasilaki E (2011) [Democratic Population Decisions Result in Robust Policy-Gradient Learning: A Parametric Study with GPU Simulations. ] PLOS One. 6:e18539 [url] [doi]
Richert M, Nageswaran JM, Dutt N and Krichmar JL (2011) An efficient simulation environment for modeling large-scale cortical processing. Front Neuroinform. 5:19 [doi]
Ren Q, Kolwankar KM., Samal A and Jost J (2010) [STDP-driven networks and the C. elegans neuronal network. ] Physica A: Statistical Mechanics and its Applications. 389:3900–3914 [url] [doi]
Ren Q, Kolwankar KM., Areejit Samal and Jost J (2012) [Hopf bifurcation in the evolution of STDP-driven networks. ] Physical review. 86:13 [html] [doi]
Rast A., Navaridas J, Jin X, Galluppi F, Plana LA., Miguel-Alonso J, Patterson C, Luján M and Furber S (2012) Managing Burstiness and Scalability in Event-Driven Models on theSpiNNaker Neuromimetic System. Springer US International Journal of Parallel Programming. 40:553–582 [doi]
Rast A, Galluppi F, Davies S, Plana L, Patterson C, Sharp T, Lester D and Furber S (2011) Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware. Neural Netw. 24:961–978 [doi]
R. F, E. S and L. M (2011) Dynamic Model of the dLGN Push-Pull Circuitry. Springer Berlin Heidelberg. 6686:129–137 [doi]
Pyka M, Klatt S and Cheng S (2014) [Parametric Anatomical Modeling: A method for modeling the anatomical layout of neurons and their projections. ] Frontiers in Neuroanatomy. 8:91 [url] [doi]
Probst D, Petrovici MA, Bytschok I, Bill J, Pecevski D, Schemmel J and Meier K (2015) [Probabilistic Inference in Discrete Spaces Can Be Implemented into Networks of LIF Neurons. ] Frontiers in Computational Neuroscience. 9:13 [url] [doi]
Probst D, Maass W, Markram H and Gewaltig MO (2012) Liquid Computing in a Simplified Model of Cortical Layer IV: Learning to Balance a Ball. Springer Berlin Heidelberg. 7552:209–216 [doi]
Potjans W, Morrison A and Diesmann M (2009) A spiking neural network model of an actor-critic learning agent. Neural Comput. 21:301–339 [doi]
Potjans W, Morrison A and Diesmann M (2010) Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity. Front Comput Neurosci. 4:141 [doi]
Potjans W, Diesmann M. and Morrison A. (2011) [An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning. ] Public Library of Science PLoS Computational Biology. 7:e1001133 [url] [doi]
Potjans T. and Diesmann M (2013) Multi-population Network Models of the Cortical Microcircuit. Springer Netherlands:91–96 [doi]
Plesser HE and Diesmann M (2009) Simplicity and efficiency of integrate-and-fire neuron models. Neural Comput. 21:353–359 [doi]
Plesser HE, Eppler JM, Morrison A, Diesmann M and Gewaltig MO (2007) Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers. In A.-M. Kermarrec and L. Bougé and T. Priol, Euro-Par 2007: Parallel Processing. Springer-Verlag. 4641:672–681 [doi]
Phoka E, Wildie M, Schultz SR and Barahona M (2012) Sensory experience modifies spontaneous state dynamics in a large-scale barrel cortical model. J Comput Neurosci. 33:323–339 [doi]
Philipp S, Grübl A, Meier K and Schemmel J (2007) Interconnecting VLSI Spiking Neural Networks Using Isochronous Connections. Springer Berlin Heidelberg. 4507:471–478 [doi]
Philip J. Tully MH. H and Lansner A (2014) [Synaptic and nonsynaptic plasticity approximating probabilistic inference. ] Frontiers in Synaptic Neuroscience. 6:8 [url] [doi]
Pfeil T, Potjans TC, Schrader S, Potjans W, Schemmel J, Diesmann M and Meier K (2012) Is a 4-bit synaptic weight resolution enough? - constraints on enabling spike-timing dependent plasticity in neuromorphic hardware. Front Neurosci. 6:90 [doi]
Pfeil T, Jordan J, Tetzlaff T, Grübl A, Schemmel J, Diesmann M and Meier K (2016) [Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study. ] American Physical Society Phys. Rev. X. 6:021023 [url] [doi]
Petrovici MA., Vogginger B, Müller P, Breitwieser O, Lundqvist M, Muller L, Ehrlich M, Destexhe A, Lansner A, Schüffny R, Schemmel J and Meier K (2014) [Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms. ] Public Library of Science PLoS ONE. 9:e108590 [url] [doi]
Pernice V, Staude B, Cardanobile S and Rotter S (2011) How structure determines correlations in neuronal networks. PLoS Comput Biol. 7:e1002059 [doi]
Pernice V, Staude B, Cardanobile S and Rotter S (2012) Recurrent interactions in spiking networks with arbitrary topology. Phys Rev E Stat Nonlin Soft Matter Phys. 85:031916 [doi]
Pernice V, Deger M, Cardanobile S and Rotter S (2013) The relevance of network micro-structure for neural dynamics. Frontiers in Computational Neuroscience. 7:72 [doi]
Perez T and Uchida A (2011) Reliability and synchronization in a delay-coupled neuronal network with synaptic plasticity. American Physical Society Phys. Rev. E. 83:061915 [doi]
Pecevski D, Natschläger T and Schuch K (2009) PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python. Front Neuroinform. 3:11 [doi]
Parekh R and Ascoli GA. (2013) [Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience. ] Neuron. 77:1017–1038 [url] [doi]
Pani D, Meloni P, Tuveri G, Palumbo F, Massobrio P and Raffo L (2017) [An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks. ] Frontiers Media S.A. Frontiers in Neuroscience. 11:90– [url] [doi]
Okun M, Yger P, Marguet SL, Gerard-Mercier F, Benucci A, Katzner S, Busse L, Carandini M and Harris KD (2012) Population rate dynamics and multineuron firing patterns in sensory cortex. J Neurosci. 32:17108–17119 [doi]
Nowke C, Zielasko D, Weyers B, Hentschel B, Peyser A and Kuhlen T (2015) [Integrating Visualizations into Modeling NEST Simulations. ] Frontiers in Neuroinformatics. 9:29 [url] [doi]
Nordlie E, Tetzlaff T and Einevoll GT (2010) Rate Dynamics of Leaky Integrate-and-Fire Neurons with Strong Synapses. Front Comput Neurosci. 4:149 [doi]
Nordlie E and Plesser HE (2010) Visualizing neuronal network connectivity with connectivity pattern tables. Front Neuroinform. 3:39 [doi]
Nordlie E, Gewaltig MO and Plesser HE (2009) Towards reproducible descriptions of neuronal network models. PLoS Comput Biol. 5:e1000456 [doi]
Naveros F., Luque N.R., Garrido J.A., Carrillo R.R., Anguita M. and Ros E. (2015) A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study. Neural Networks and Learning Systems, IEEE Transactions on. 26:1567–1574 [doi]
Naveros F, Garrido JA, Carrillo RR, Ros E and Luque NR (2017) [Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks. ] Frontiers Media S.A. Frontiers in Neuroinformatics. 11:7– [url] [doi]
Nair M, Nair B and Diwakar S (2015) Computational Intelligence Methods for Bioinformatics and Biostatistics: 11th International Meeting, CIBB 2014, Cambridge, UK, June 26-28, 2014, Revised Selected Papers. Springer International Publishing:285–297 [doi]
Muntean I.L., Joldos M., Ban I.R. and Morariu I.M. (2011) Parallel Investigations of the Dynamics of Neural Microcircuits. , Parallel and Distributed Computing (ISPDC), 2011 International Symposium on. :266–269 [doi]
Mundy A., Knight J., Stewart T.C. and Furber S. (2015) An efficient SpiNNaker implementation of the Neural Engineering Framework. , Neural Networks (IJCNN), 2015 International Joint Conference on. :1–8 [doi]
Muller L, Reynaud A, Chavane F and Destexhe A (2014) [The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave. ] Nature Communications. 5 [html] [doi]
Muller L and Destexhe A (2012) [Propagating waves in thalamus, cortex and the thalamocortical system: Experiments and models. ] Journal of Physiology-Paris. 106:222–238 [url] [doi]
Muller E, Buesing L, Schemmel J and Meier K (2007) Spike-frequency adapting neural ensembles: beyond mean adaptation and renewal theories. Neural Comput. 19:2958–3010 [doi]
Mulas M, Massobrio P, Martinoia S and Chiappalone M (2010) A simulated neuro-robotic environment for bi-directional closed-loop experiments. SP Versita Paladyn. 1:179–186 [doi]
Mulas M and Massobrio P (2013) [NeuVision: A novel simulation environment to model spontaneous and stimulus-evoked activity of large-scale neuronal networks. ] Neurocomputing. 122:441–457 [url] [doi]
Morrison A, Mehring C, Geisel T, Aertsen A. D. and Diesmann M (2005) [Advancing the boundaries of high-connectivity network simulation with distributed computing. ] Neural Comput. 17:1776–1801 [url] [doi]
Morrison A, Diesmann M and Gerstner W (2008) Phenomenological models of synaptic plasticity based on spike timing. Biol Cybern. 98:459–478 [doi]
Moren J, Shibata T and Doya K (2013) The Mechanism of Saccade Motor Pattern Generation Investigated by a Large-Scale Spiking Neuron Model of the Superior Colliculus. PLOS One. 8:e57134 [doi]
Moren J, Shibata T and Doya K (2010) Toward a Spiking-Neuron Model of the Oculomotor System. Springer Berlin Heidelberg. 6226:104–113 [doi]
Mohemmed A, Schliebs S, Matsuda S and Kasabov N (2012) SPAN: Spike pattern association neuron for learning spatio-temporal spike patterns. Int J Neural Syst. 22:1250012 [doi]
Mohemmed A, Schliebs S, Matsuda S and Kasabov N (2011) Method for Training a Spiking Neuron to Associate Input-Output Spike Trains. Springer Berlin Heidelberg. 363:219–228 [doi]
Mohemmed A, Schliebs S and Kasabov N (2011) SPAN: A Neuron for Precise-Time Spike Pattern Association. Springer Berlin Heidelberg. 7063:718–725 [doi]
Mohemmed A., Matsuda S., Schliebs S., Dhoble K. and Kasabov N. (2011) [Optimization of Spiking Neural Networks with dynamic synapses for spike sequence generation using PSO. ], Neural Networks (IJCNN), The 2011 International Joint Conference on. :2969–2974 [url] [doi]
Minkovich K., Thibeault C.M., O'Brien M.J. and Nogin A. (2014) [HRLSim: A High Performance Spiking Neural Network Simulator for GPGPU Clusters. ] Neural Networks and Learning Systems, IEEE Transactions on. 25:316–331 [url] [doi]
Millner S (2013) [Development of a Multi-Compartment Neuron Model Emulation. ] [url] [doi]
Meier R, Kumar A, Schulze-Bonhage A and Aertsen A (2007) [Comparison of dynamical states of random networks with human EEG. ] Neurocomputing. 70:1843–1847 [url] [doi]
Meier R, Egert U, Aertsen A and Nawrot MP. (2008) [FIND — A unified framework for neural data analysis. ] Neural Networks. 21:1085–1093 [url] [doi]
Mehring C, Hehl U, Kubo M, Diesmann M and Aertsen A (2003) Activity dynamics and propagation of synchronous spiking in locally connected random networks. Biol Cybern. 88:395–408 [doi]
Mayor J and Gerstner W (2005) [Signal buffering in random networks of spiking neurons: Microscopic versus macroscopic phenomena. ] American Physical Society Phys. Rev. E. 72:051906 [url] [doi]
Mayor J and Gerstner W (2005) Noise-enhanced computation in a model of a cortical column. Cognitive Neurosci. 16:1237–1240
Mattioni M and Novère NL (2013) Integration of Biochemical and Electrical Signaling-Multiscale Model of the Medium Spiny Neuron of the Striatum. PLOS One. 8:e66811 [doi]
Mattioni M, Cohen U and Novère NL (2012) Neuronvisio: A Graphical User Interface with 3D Capabilities for NEURON. Front Neuroinform. 6:20 [doi]
Martinez-Canada P, Morillas C and Pelayo F (2017) [A Conductance-Based Neuronal Network Model for Color Coding in the Primate Foveal Retina. ] Springer International Publishing:63–74 [url] [doi]
Marre O, Yger P, Davison AP and Frégnac Y (2009) Reliable recall of spontaneous activity patterns in cortical networks. J Neurosci. 29:14596–14606 [doi]
Manninen T, Hituri K, Kotaleski JH, Blackwell KT and Linne ML (2010) [Postsynaptic signal transduction models for long-term potentiation and depression. ] Front Comput Neurosci. 4:152 [url] [doi]
Man Yi Yim AKAA and Rotter S (2014) Impact of correlated inputs to neurons: modeling observations from in vivo intracellular recordings. Journal of Computational Neuroscience. 37:293–304 [doi]
Mäki-Marttunen T, Aćimović J, Ruohonen K and Linne ML (2013) Structure-Dynamics Relationships in Bursting Neuronal Networks Revealed Using a Prediction Framework. PLOS One. 8:e69373 [doi]
Mäki-Marttunen T, Acimovic J, Nykter M, Kesseli J, Ruohonen K, Yli-Harja O and Linne ML (2011) [Information Diversity in Structure and Dynamics of Simulated Neuronal Networks. ] Front Comput Neurosci. 5:26 [url] [doi]
Majumdar A, Sivagnanam S, Carnevale NT, Yoshimoto K, Gleeson P, Quintana A and Silver RA (2016) Neuroscience Gateway ? Cyberinfrastructure Providing Supercomputing Resources for Large Scale Computational Neuroscience Research. Frontiers in Neuroinformatics [doi]
Lytton WW., Seidenstein AH., Dura-Bernal S, McDougal RA., Schãärmann F and Hines ML. (2016) Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. MIT Press Neural Computation. 28:2063–2090 [doi]
Lindén H, Tetzlaff T, Potjans TC, Pettersen KH, Grün S, Diesmann M and Einevoll GT (2011) Modeling the spatial reach of the LFP. Neuron. 72:859–872 [doi]
Lindahl M, Sarvestani IK, Ekeberg Ö and Kotaleski JH (2013) Signal enhancement in the output stage of the basal ganglia by synaptic short-term plasticity in the direct, indirect, and hyperdirect pathways. Front Comput Neurosci. 7:Article 76:1–19 [doi]
Linaro D., Poggi T. and Storace M. (2010) [Experimental bifurcation diagram of a circuit-implemented neuron model. ] Physics Letters A. 374:4589–4593 [url] [doi]
Lim H.K., Keniston L.P., Shin J.H., nguyen C., Meredith M.A. and Cios K.J. (2010) [A Neuronal Multisensory Processing Simulator. ], Neural Networks (IJCNN), The 2010 International Joint Conference on. :1–7 [url] [doi]
Leukhin A, Talanov M, Sozutov I, Vallverdu J and Toschev A (2016) [Simulation of a Fear-like State on a Model of Dopamine System of Rat Brain. ] Springer International Publishing:121–126 [url] [doi]
Lensjo KK, Lepperod ME, Dick G, Hafting T and Fyhn M (2017) [Removal of Perineuronal Nets Unlocks Juvenile Plasticity Through Network Mechanisms of Decreased Inhibition and Increased Gamma Activity. ] Society for Neuroscience Journal of Neuroscience. 37:1269–1283 [url] [doi]
Lengler J, Jug F and Steger A (2013) [Reliable Neuronal Systems: The Importance of Heterogeneity. ] PLOS One. 8:12:e80694 [url] [doi]
Lefort S, Tomm C, Sarria J.C.F and Petersen CC.H. (2008) [The Excitatory Neuronal Network of the C2 Barrel Column in Mouse Primary Somatosensory Cortex. ] Neuron. 10:1016 [pdf] [doi]
Lee JH, Koch C and Mihalas S (2017) [A Computational Analysis of the Function of Three Inhibitory Cell Types in Contextual Visual Processing. ] Frontiers Media S.A. Frontiers in Computational Neuroscience. 11:28 [url] [doi]
Lee JH (2017) [Disrupted cholinergic modulation can underlie abnormal gamma rhythms in schizophrenia and auditory hallucination. ] Journal of Computational Neuroscience. 43:173–187 [url] [doi]
Lee J, Tsunada J and Cohen YE. (2013) A Model of the Differential Representation of Signal Novelty in the Local Field Potentials and Spiking Activity of the Ventrolateral Prefrontal Cortex. Neural Comput. 25:157–185 [doi]
Le Mouel C, Harris K. and Yger P (2014) Supervised learning with decision margins in pools of spiking neurons. Springer US Journal of Computational Neuroscience. 37:333–344 [doi]
Lansner A and Diesmann M (2012) Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers Computational Systems Neurobiology. Springer Netherlands:283–315 [doi]
Kupper R, Gewaltig MO, Körner U and Körner E (2005) [Spike-latency codes and the effect of saccades. ] Neurocomputing. 65–66:189–194 [url] [doi]
Kunkel S, Schmidt M, Eppler JM, Plesser HE, Masumoto G, Igarashi J, Ishii S, Fukai T, Morrison A, Diesmann M and Helias M (2014) Spiking network simulation code for petascale computers. Frontiers in Neuroinformatics. 8 [doi]
Kunkel S, Schmidt M, Eppler JM, Plesser HE, Igarashi J, Masumoto G, Fukai T, Ishii S, Morrison A, Diesmann M and Helias M (2013) From laptops to supercomputers: a single highly scalable code base for spiking neuronal network simulations. [doi]
Kunkel S and Schenck W (2017) [The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code. ] Frontiers in Neuroinformatics. 11:40 [url] [doi]
Kunkel S, Potjans TC, Eppler JM, Plesser HE, Morrison A and Diesmann M (2012) Meeting the memory challenges of brain-scale network simulation. Front Neuroinform. 5:35 [doi]
Kunkel S, Diesmann M and Morrison A (2011) Limits to the development of feed-forward structures in large recurrent neuronal networks. Frontiers in Computational Neuroscience. 4:- [doi]
Kumar A, Schrader S, Aertsen A and Rotter S (2008) The high-conductance state of cortical networks. Neural Comput. 20:1–43 [doi]
Kumar A, Rotter S and Aertsen A (2008) Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J Neurosci. 28:5268–5280 [doi]
Kumar A, Cardanobile S, Rotter S and Aertsen A (2011) [The Role of Inhibition in Generating and Controlling Parkinsons Disease Oscillations in the Basal Ganglia. ] Frontiers in Systems Neuroscience. 5:86 [url] [doi]
Kukin K. and Sboev A. (2015) Comparison of learning methods for spiking neural networks. Pleiades Publishing Optical Memory and Neural Networks. 24:123–129 [doi]
Kühn T and Helias M (2017) [Locking of correlated neural activity to ongoing oscillations. ] Public Library of Science PLOS Computational Biology. 13:1-32 [url] [doi]
Kriener B, Tetzlaff T, Aertsen A, Diesmann M and Rotter S (2008) Correlations and population dynamics in cortical networks. Neural Comput. 20:2185–2226 [doi]
Kriener B, Helias M, Rotter S, Diesmann M and Einevoll GT. (2014) How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime. Frontiers in Computational Neuroscience. 7:187 [doi]
Kriener B, Helias M, Aertsen A and Rotter S (2009) Correlations in spiking neuronal networks with distance dependent connections. J Comput Neurosci. 27:177–200 [doi]
Kriener B, Enger H, Tetzlaff T, Plesser HE, Gewaltig MO and Einevoll GT (2014) [Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses. ] Frontiers Media SA Frontiers in computational neuroscience. 8:136 [url] [doi]
Kriener B (2012) How synaptic weights determine stability of synchrony in networks of pulse-coupled excitatory and inhibitory oscillators. Chaos. 22:033143 [doi]
Kremkow J, Perrinet LU, Masson GS and Aertsen A (2010) Functional consequences of correlated excitatory and inhibitory conductances in cortical networks. J Comput Neurosci. 28:579–594 [doi]
Kremkow J, Aertsen A and Kumar A (2010) Gating of signal propagation in spiking neural networks by balanced and correlated excitation and inhibition. The Journal of neuroscience : the official journal of the Society for Neuroscience. 30:15760–15768 [doi]
Kolwankar KM., Ren Q, Samal A and Jost J (2011) Learning and structure of neuronal networks. Springer-Verlag Pramana. 77:817–826 [doi]
Keshavarz-Hedayati B, Dimopoulos N and Babul A (2017) [A Study on the Effects of Lesions on CA3b in Hippocampus. ] Springer International Publishing:474–482 [url] [doi]
Kayraklioglu E., El-Ghazawi T. and Bozkus Z. (2015) Accelerating Brain Simulations on Graphical Processing Units. , Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on. :556-560 [doi]
Kapucu F. E., VÃ¤lkki I., Christophe F., Tanskanen J. M. A., Johansson J., Mikkonen T. and Hyttinen J. A. K. (2017) On electrophysiological signal complexity during biological neuronal network development and maturation. , 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). :3333-3338 [doi]
Kaplan B.A., Khoei M.A., Lansner A. and Perrinet L.U. (2014) Signature of an anticipatory response in area VI as modeled by a probabilistic model and a spiking neural network. , Neural Networks (IJCNN), 2014 International Joint Conference on. :3205–3212 [doi]
Kaplan B., Bruderle D., Schemmel J. and Meier K. (2009) [High-conductance states on a neuromorphic hardware system. ], Neural Networks, 2009. IJCNN 2009. International Joint Conference on. :1524–1530 [url] [doi]
Kaplan BA., Lansner A, Masson GS. and Perrinet LU. (2013) [Anisotropic connectivity implements motion-based prediction in a spiking neural network. ] Front Comput Neurosci. 7:112 [url] [doi]
Kannon T, Inagaki K, Kamiji NL, Makimura K and Usui S (2011) PLATO: data-oriented approach to collaborative large-scale brain system modeling. Neural Netw. 24:918–926 [doi]
Kamps Mde (2012) [Towards truly human-level intelligence in artificial applications. ] Cognitive Systems Research. 14:1–9 [url] [doi]
Kamali, Sarvestani I, Kozlov A, Harischandra N, Grillner S and Ekeberg Ö (2012) A computational model of visually guided locomotion in lamprey. Springer-Verlag Biological Cybernetics. -:1–16 [doi]
Jovanovic S and Rotter S (2016) [Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks. ] Public Library of Science PLoS Comput Biol. 12:1-28 [url] [doi]
Jones B, Jin Y, Yao X and Sendhoff B (2009) Evolution of Neural Organization in a Hydra-Like Animat. Springer Berlin Heidelberg. 5506:216–223 [doi]
Jitsev J, Abraham N, Morrison A and Tittgemeyer M (2013) Functional role of opponent, dopamine modulated D1/D2 plasticity in reinforcement learning. [doi]
Jitsev J, Abraham N, Morrison A and Tittgemeyer M (2012) Learning from Delayed Reward und Punishment in a Spiking Neural Network Model of Basal Ganglia with Opposing D1/D2 Plasticity. Springer Berlin Heidelberg. 7552:459–466 [doi]
Jin Y, Wen R and Sendhoff B (2007) Evolutionary Multi-objective Optimization of Spiking Neural Networks. Springer Berlin Heidelberg. 4668:370–379 [doi]
Jin Y, Schramm L and Sendhoff B (2009) A Gene Regulatory Model for the Development of Primitive Nervous Systems. Springer Berlin Heidelberg. 5506:48–55 [doi]
Ji Y, Zhang Y, Liu H and Zheng W (2016) Optimized Mapping Spiking Neural Networks onto Network-on-Chip. Springer International Publishing:38–52 [doi]
Jahnke S, Timme M and Memmesheimer RM (2012) Guiding Synchrony through Random Networks. American Physical Society Phys. Rev. X. 2:041016 [doi]
Jahnke S, Memmesheimer RM and Timme M (2013) Propagating synchrony in feed-forward networks. Front Comput Neurosci. 7:153 [doi]
Jahnke S, Memmesheimer RM and Timme M (2014) [Oscillation-Induced Signal Transmission and Gating in Neural Circuits. ] Public Library of Science PLoS Comput Biol. 10:e1003940 [url] [doi]
Wu JQ., Peters GJ., Rittner P, Cleland TA. and Smith DM. (2014) [The hippocampus, medial prefrontal cortex, and selective memory retrieval: Evidence from a rodent model of the retrieval-induced forgetting effect. ] Wiley Hippocampus. 24:1070–1080 [url] [doi]
Saito J. H., Mari J.-F., Pedrino E., Destro-Filho J. B. and Nicoletti M. C. (2013) [Simulated Activation Patterns of Biological Neurons Cultured onto a Multi-Electrode Array Based on a Modified Izhikevich's Model. ] Fundamenta Informaticae. 124:111–132 [url] [doi]
Ippen T, Eppler JM., Plesser HE and Diesmann M (2017) [Constructing neuronal network models in massively parallel environments. ] Front. Neuroinform.. 11:30 [url] [doi]
Indiveri G, Linares-Barranco B, Hamilton TJ, van Schaik A, Etienne-Cummings R, Delbruck T, Liu SC, Dudek P, Häfliger P, Renaud S, Schemmel J, Cauwenberghs G, Arthur J, Hynna K, Folowosele F, Saighi S, Serrano-Gotarredona T, Wijekoon J, Wang Y and Boahen K (2011) Neuromorphic silicon neuron circuits. Frontiers in Neuroscience. 5:73 [doi]
Ince RA.A., Mazzoni A, Petersen RS. and Panzeri S (2010) Open Source Tools for the Information Theoretic Analysis of Neural Data. Frontiers in Neuroscience. 4:62 [doi]
Igarashi J, Shouno O, Fukai T and Tsujino H (2011) Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units. Neural Netw. 24:950–960 [doi]
Huyck C, Evans C and Mitchell I (2015) [A comparison of simple agents implemented in simulated neurons. ] Biologically Inspired Cognitive Architectures:- [url] [doi]
Hull MJ. and Willshaw DJ. (2013) [morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python. ] Front Neuroinform.. 7:47 [url] [doi]
Hugo Cornelis AD. CJM. B (2011) [A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture. ] PLOS One. 7:e28956 [url] [doi]
Holt AB. and Netoff TI. (2013) [Computational modeling of epilepsy for an experimental neurologist. ] Experimental Neurology. 244:75–86 [url] [doi]
Hinkel G, Groenda H, Krach S, Vannucci L, Denninger O, Cauli N, Ulbrich S, Roennau A, Falotico E, Gewaltig MO, Knoll A and Dillmann R (2017) [A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks. ] Journal of Intelligent Robotic Systems. 85:71–91 [url] [doi]
Hernandez OE and Zurek EE (2013) Teaching and learning the Hodgkin-Huxley model based on software developed in NEURONs programming language hoc. BioMed Central BMC Medical Education. 13:1–9 [doi]
Henker S, Partzsch J and Schüffny R (2012) Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks. J Comput Neurosci. 32:309–326 [doi]
Helias M, Tetzlaff T and Diesmann M (2013) [Echoes in correlated neural systems. ] New Journal of Physics. 15:023002 [url] [doi]
Helias M, Tetzlaff T and Diesmann M (2014) The correlation structure of local neuronal networks intrinsically results from recurrent dynamics. PLoS Computational Biology. 10:e1003428 [doi]
Helias M, Rotter S, Gewaltig MO and Diesmann M (2008) Structural plasticity controlled by calcium based correlation detection. helias@bccn.uni-freiburg.de. Front Comput Neurosci. 2:7 [doi]
Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai T, Morrison A and Diesmann M (2012) Supercomputers ready for use as discovery machines for neuroscience. Front Neuroinform. 6:26 [doi]
Helias M, Grytskyy D, Tetzlaff T and Diesmann M (2011) Model-invariant features of correlations in recurrent networks. Frontiers in Computational Neuroscience. 10:3389 [doi]
Helias M, Deger M, Rotter S and Diesmann M (2010) Instantaneous non-linear processing by pulse-coupled threshold units. PLoS Comput Biol. 6:- [doi]
Helias M, Deger M, Rotter S and Diesmann M (2011) Finite post synaptic potentials cause a fast neuronal response. Frontiers in Neuroscience. 5:19 [doi]
Helias M, Deger M, Diesmann M and Rotter S (2010) Equilibrium and Response Properties of the Integrate-and-Fire Neuron in Discrete Time. Front Comput Neurosci. 3:29 [doi]
Heiberg T, Kriener B, Tetzlaff T, Casti A, Einevoll GT. and Plesser HE. (2013) Firing-rate models capture essential response dynamics of LGN relay cells. Springer US Journal of Computational Neuroscience:1–17 [doi]
Heiberg T, Hagen E, Halnes G and Einevoll GT. (2016) [Biophysical Network Modelling of the dLGN Circuit: Different Effects of Triadic and Axonal Inhibition on Visual Responses of Relay Cells. ] Public Library of Science PLoS Comput Biol. 12:1-38 [url] [doi]
Harischandra N, Knuesel J, Kozlov A, Bicanski A, Cabelguen JM, Ijspeert AJ and Ekeberg Ö (2011) Sensory feedback plays a significant role in generating walking gait and in gait transition in salamanders: A simulation study. Frontiers in Neurorobotics. 5:- [doi]
Hanuschkin A, Kunkel S, Helias M, Morrison A and Diesmann M (2010) A general and efficient method for incorporating precise spike times in globally time-driven simulations. Frontiers Research Foundation Frontiers in Neuroinformatics. 4:113 [doi]
Hanuschkin A., Herrmann J., Morrison A. and Diesmann M. (2011) [Compositionality of arm movements can be realized by propagating synchrony. ] Springer Science + Business Media B.V Journal of computational neuroscience. 30:675–697 [url] [doi]
Hanuschkin A, Diesmann M and Morrison A (2011) A reafferent and feed-forward model of song syntax generation in the Bengalese finch. Journal of Computational Neuroscience. 31:509–532 [doi]
Hahne J, Helias M, Kunkel S, Igarashi J, Kitayama I, Wylie B, Bolten M, Frommer A and Diesmann M (2016) [Including Gap Junctions into Distributed Neuronal Network Simulations. ] Springer International Publishing:43–57 [url] [doi]
Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A and Diesmann M (2015) A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers in Neuroinformatics. 9:22 [doi]
Hahne J, Dahmen D, Schuecker J, Frommer A, Bolten M, Helias M and Diesmann M (2017) [Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator. ] Frontiers in Neuroinformatics. 11:34 [url] [doi]
Hahn G, Bujan AF., Frégnac Y, Aertsen A and Kumar A (2014) [Communication through Resonance in Spiking Neuronal Networks. ] Public Library of Science PLoS Comput Biol. 10:e1003811 [url] [doi]
Hagen E, Stavrinou ML, Linden H, Tetzlaff T, Albada Svan, Dahmen D, Diesmann M, Gruen S and Einevoll GT (2013) [Hybrid scheme for modeling LFPs from spiking cortical network models. ] BMC Neuroscience. 14:P119 [url] [doi]
Hagen E, Ness TV., Khosrowshahi A, Sørensen C, Fyhn M, Hafting T, Franke F and Einevoll GT. (2015) [ViSAPy: A Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms. ] Journal of Neuroscience Methods:- [url] [doi]
Guyon I, Battaglia D, Guyon A, Lemaire V, Orlandi JG., Saeed M, Soriano J, Statnikov A, Stetter O and Ray B (2014) [Design of the First Neuronal Connectomics Challenge: From Imaging to Connectivity. ]:2600-2607 [url] [doi]
Gürel T, Raedt LD and Rotter S (2007) [Ranking neurons for mining structure-activity relations in biological neural networks: NeuronRank. ] Neurocomputing. 70:1897–1901 [url] [doi]
Gürel T, Raedt L and Rotter S (2007) Mining Structure-Activity Relations in Biological Neural Networks using NeuronRank. Springer Berlin Heidelberg. 77:49–65 [doi]
Grytskyy D, Tetzlaff T, Diesmann M and Helias M (2013) A unified view on weakly correlated recurrent networks. Frontiers in Computional Neuroscience. 7:131 [doi]
Goodman DFM and Brette R (2009) The brian simulator. Front Neurosci. 3:192–197 [doi]
Goodman DFM (2010) Code generation: a strategy for neural network simulators. Neuroinformatics. 8:183–196 [doi]
Goodman D and Brette R (2008) Brian: a simulator for spiking neural networks in python. Front Neuroinform. 2:5 [doi]
Gollo LL., Mirasso C and Villa AE.P. (2010) [Dynamic control for synchronization of separated cortical areas through thalamic relay. ] Neuroimage. 52:947–955 [url] [doi]
Goedeke S and Diesmann M (2008) [The mechanism of synchronization in feed-forward neuronal networks. ] New Journal of Physics. 10:015007 [url] [doi]
Gleeson P, Steuber V and Silver R. A (2007) [neuroConstruct: A Tool for Modeling Networks of Neurons in 3D Space. ] Neuron. 54:219–235 [url] [doi]
Gleeson P, Silver R.A and Steuber V (2010) Computer Simulation Environments. Springer New York. 5:593–609 [doi]
Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, Farinella M, Morse TM, Davison AP, Ray S, Bhalla US, Barnes SR, Dimitrova YD and Silver R. A (2010) NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput Biol. 6:e1000815 [doi]
Gewaltig MO, Richter A and Kupper R (2002) BLISS: towards the simulation of brain-like systems. Neurocomputing. 44-46:805–810 [doi]
Gewaltig MO, Morrison A and Plesser H (2012) NEST by Example: An Introduction to the Neural Simulation Tool NEST. Springer Netherlands:533–558 [doi]
Gewaltig MO and Diesmann M (2007) NEST:NEural Simulation Tool. Scholarpedia. 2:1430 [file]
Gerhard F, Deger M and Truccolo W (2017) [On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs. ] Public Library of Science PLOS Computational Biology. 13:1-31 [url] [doi]
Gamez D (2007) SpikeStream: A Fast and Flexible Simulator of Spiking Neural Networks. Springer Berlin Heidelberg. 4668:360–369 [doi]
Galluppi F, Davies S, Rast A, Sharp T, Plana LA. and Furber S (2012) A hierachical configuration system for a massively parallel neural hardware platform. , Proceedings of the conference on Computing Frontiers. ACM:183–192 [doi]
Fidjeland A., Gamez D, Shanahan M. and Lazdins E (2012) Three Tools for the Real-Time Simulation of Embodied Spiking Neural Networks Using GPUs. Springer-Verlag Neuroinformatics:1–24 [doi]
Ferreiroa R and Sánchez E (2013) [Functional properties of a realistic model of dLGN. ] Neurocomputing. 114:36–44 [url] [doi]
Ferreiroa R and Sánchez E (2013) Contrast Enhancement Mechanisms in the Retinothalamic Circuitry. Springer Berlin Heidelberg. 7930:26–36 [doi]
Farkhooi F and van Vreeswijk C (2015) Renewal Approach to the Analysis of the Asynchronous State for Coupled Noisy Oscillators. PHYSICAL REVIEW LETTERS. 115:038103 [doi]
Farkhooi F, Froese A, Muller E, Menzel R and Nawrot MP. (2012) [Cellular Adaptation Accounts for the Sparse and Reliable Sensory Stimulus Representation. ] Quantitative Biology:Neurons and Cognition. -:arXiv:1210.7165 [q-bio.NC] [url] [doi]
Farkhooi F, Froese A, Muller E, Menzel R and Nawrot MP. (2013) [Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways. ] PLOS Comput Bio. 9:10: e1003251 [url] [doi]
Ercelik E. and Sengor N.S. (2015) A neurocomputational model implemented on humanoid robot for learning action selection. , Neural Networks (IJCNN), 2015 International Joint Conference on. :1–6 [doi]
Eppler JM, Helias M, Muller E, Diesmann M and Gewaltig MO (2008) PyNEST: A Convenient Interface to the NEST Simulator. Front Neuroinform. 2:12 [doi]
Eppler J., Plesser H., Morrison A, Diesmann M and Gewaltig MO (2007) Multithreaded and Distributed Simulation of Large Biological Neuronal Networks. Springer Berlin Heidelberg. 4757:391–392 [doi]
Eppler J (2009) [A Python interface to NEST. ] The Neuromorphic Engineer [pdf] [doi]
Einevoll GT. (2009) [Sharing with Python. ] Front Neurosci.. 3:334–335 [url] [doi]
Eichner H, Klug T and Borst A (2009) Neural simulations on multi-core architectures. Front Neuroinform. 3:21 [doi]
Ebert M, Hauptmann C and Tass P (2014) Coordinated reset stimulation in a large-scale model of the STN-GPe circuit. Frontiers in Computational Neuroscience. 8:154 [doi]
Eastridge BJ, Hamilton EC, O'Keefe GE, Rege RV, Valentine RJ, Jones DJ, Tesfay S and Thal ER (2003) [Effect of sleep deprivation on the performance of simulated laparoscopic surgical skill. ] Am J Surg. 186:169–174 [url] [doi]
Duarte RCF and Morrison A (2014) Dynamic stability of sequential stimulus representations in adapting neuronal networks. Frontiers in Computational Neuroscience. 8:124 [doi]
Drewes R, Zou Q and Goodman PH. (2009) [Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator. ] Front Neuroinformatics.. 3:16 [url] [doi]
Dr. Ing. Weyers B, Nowke C, Hänel C, Zielasko D, Hentschel B and Kuhlen T (2015) [The Human Brain Project – Chances and Challenges for Cognitive Systems. ] DuEPublico [url] [doi]
Djurfeldt M., Lundqvist M., Johansson C., Rehn M., Ekeberg O. and Lansner A. (2008) Brain-scale simulation of the neocortex on the IBM Blue Gene L supercomputer. IBM Journal of Research and Development. 52:31–41 [doi]
Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Kotaleski JH and Ekeberg O (2010) Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics. 8:43–60 [doi]
Djurfeldt M, Ekeberg O and Lansner A (2008) Large-scale modeling - a tool for conquering the complexity of the brain. Front Neuroinform. 2:1 [doi]
Djurfeldt M, Davison AP. and Eppler JM. (2014) [Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. ] Front Neuroinform. 8:43 [url] [doi]
Djurfeldt M (2012) The connection-set algebra–a novel formalism for the representation of connectivity structure in neuronal network models. Neuroinformatics. 10:287–304 [doi]
Dinkelbach HÜ, Vitay J, Beuth F and Hamker FH (2012) Comparison of GPU- and CPU-implementations of mean-firing rate neural networks on parallel hardware. Network. 23:212–236 [doi]
Diesmann M, Gewaltig MO and Aertsen A (1999) Stable propagation of synchronous spiking in cortical neural networks. Nature. 402:529–533 [doi]
Diesmann M and Gewaltig MO (2001) [NEST: An Environment for Neural Systems Simulations. ] Forschung und wisschenschaftliches Rechnen, Beiträge zum Heinz-Billing-Preis. 58:43-70 [url]
Diesmann M., Gewaltig M.-O. and Aertsen A. (1995) SYNOD: an Environment for Neural Systems Simulations. Language Interface and Tutorial. Weizmann Institute of Science, technical report. GC-AA-/95-3
Diaz-Pier S, Naveau M«l, Butz-Ostendorf M and Morrison A (2016) Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity. Frontiers in Neuroanatomy. 10:57 [doi]
Degterev A and Burtsev M (2016) [Simulation of Learning in Neuronal Culture. ] Springer International Publishing:47–52 [url] [doi]
Deger M, Schwalger T, Naud R and Gerstner W (2014) Fluctuations and information filtering in coupled populations of spiking neurons with adaptation. American Physical Society Phys. Rev. E. 90:062704 [doi]
Deger M, Helias M, Rotter S and Diesmann M (2012) Spike-timing dependence of structural plasticity explains cooperative synapse formation in the neocortex. PLoS Comput Biol. 8:e1002689 [doi]
Deger M, Helias M, Boucsein C and Rotter S (2012) Statistical properties of superimposed stationary spike trains. J Comput Neurosci. 32:443–463 [doi]
Davison AP., Hines ML. and Muller E (2009) [Trends in Programming Languages for Neuroscience Simulations. ] Front Neurosci.. 3:374–380 [url] [doi]
Crook SM, Bednar JA, Berger S, Cannon R, Davison AP, Djurfeldt M, Eppler J, Kriener B, Furber S, Graham B, Plesser HE, Schwabe L, Smith L, Steuber V and Albada Svan (2012) Creating, documenting and sharing network models. Network. 23:131–149 [doi]
Crook S., Davison A. and Plesser H. (2013) Learning from the Past: Approaches for Reproducibility in Computational Neuroscience. Springer New York. 9:73–102 [doi]
Cornelis H and Schutter ED (2007) [Neurospaces:Towards automated model partitioning for parallel computers. ] Neurocomputing. 70:2117–2121 [url] [doi]
Cornelis H, Rodriguez AL., Coop AD. and Bower JM. (2011) Python as a Federation Tool for GENESIS 3.0. PLOS One. 7:1 [doi]
Colliaux D, Yger P and Kaneko K (2015) Impact of sub and supra-threshold adaptation currents in networks of spiking neurons. Journal of Computational Neuroscience. 39:255–270 [doi]
Cohen MX. and Gulbinaite R (2013) [Five methodological challenges in cognitive electrophysiology. ] NeuroImage:- [url] [doi]
Clewley R (2012) [Hybrid Models and Biological Model Reduction with PyDSTool. ] PLoS Comput Biol. 8:e1002628 [url] [doi]
Chun-Wei Yuan LKBG and Leibold C (2014) [Neuronal Adaptation Translates Stimulus Gaps into a Population Code. ] PLOS One. 9:e95705 [url] [doi]
Chua Y and Morrison A (2016) Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation. Frontiers in Computational Neuroscience. 10:76 [doi]
Chua Y, Helias M and Morrison A (2015) Modeling the calcium spike as a threshold triggered fixed waveform for synchronous inputs in the fluctuation regime. Frontiers in Computational Neuroscience. 9:91 [doi]
Chris Trengove CV. L and Diesmann M (2010) Random compositional networks of synfire chains dynamically self-tune to the critical state for ongoing percolation of activity. [doi]
Chen W and Schutter ED (2014) [Python-based geometry preparation and simulation visualization toolkits for STEPS. ] Frontiers in Neuroinformatics. 8:37 [url] [doi]
Chapuis A and Tetzlaff T (2014) The variability of tidewater-glacier calving: origin of event-size and interval distributions. Journal of Glaciology. 60:622–634 [doi]
Carlson KD., Beyeler M, Dutt N and Krichmar JL. (2014) [GPGPU accelerated simulation and parameter tuning for neuromorphic applications. ]:570–577 [url] [doi]
Cannon RC., Gewaltig MO, Gleeson P, Bhalla US., Cornelis H, Hines ML., Howell FW., Muller E, Stiles JR., Wils S and De Schutter E (2007) Interoperability of Neuroscience Modeling Software: Current Status and Future Directions. Humana Press Inc Neuroinformatics. 5:127–138 [doi]
Cain N, Iyer R, Koch C and Mihalas S (2016) [The Computational Properties of a Simplified Cortical Column Model. ] Public Library of Science PLOS Computational Biology. 12:1-18 [url] [doi]
Buschermöhle M (2003) Latenzvariabilität synchroner Spikeaktivität in kortikalen Netzwerken.
Bujan AF., Aertsen A and Kumar A (2015) [Role of Input Correlations in Shaping the Variability and Noise Correlations of Evoked Activity in the Neocortex. ] The Journal of Neuroscience. 35:8611–8625 [url] [doi]
Bruzzone A., Pasquale V., Nowak P., Tessadori J., Massobrio P. and Chiappalone M. (2015) Interfacing in silico and in vitro neuronal networks. , Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. :3391–3394 [doi]
Brüderle D, Petrovici MA, Vogginger B, Ehrlich M, Pfeil T, Millner S, Grübl A, Wendt K, Müller E, Schwartz MO, Oliveira DHde, Jeltsch S, Fieres J, Schilling M, Müller P, Breitwieser O, Petkov V, Muller L, Davison AP, Krishnamurthy P, Kremkow J, Lundqvist M, Muller E, Partzsch J, Scholze S, Zühl L, Mayr C, Destexhe A, Diesmann M, Potjans TC, Lansner A, Schüffny R, Schemmel J and Meier K (2011) A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol Cybern. 104:263–296 [doi]
Brüderle D, Müller E, Davison A, Muller E, Schemmel J and Meier K (2009) Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system. Front Neuroinform. 3:17 [doi]
Brüderle D, Grübl A, Meier K, Mueller E and Schemmel J (2007) A Software Framework for Tuning the Dynamics of Neuromorphic Silicon Towards Biology. Springer Berlin Heidelberg. 4507:479–486 [doi]
Brette R and Goodman DFM (2011) Vectorized algorithms for spiking neural network simulation. Neural Comput. 23:1503–1535 [doi]
Bray LC. J, Anumandla SR., Thibeault CM., Hoang RV., Goodman PH., Dascalu SM., Bryant BD. and Jr. FC. H (2012) [Real-time human–robot interaction underlying neurorobotic trust and intent recognition. ] Neural Networks. 32:130–137 [url] [doi]
Boustani SE, Marre O, Behuret S, Baudot P, Yger P, Bal T, Destexhe A and Fregnac Y (2009) Network-state modulation of power-law frequency-scaling in visual cortical neurons. PLoS Comput Biol. 5:e1000519 [doi]
Boustani SE and Destexhe A (2009) A Master Equation Formalism for Macroscopic Modeling of Asynchronous Irregular Activity States. Neural Comput. 21:46–100 [doi]
Bouchain D, Hauser F and Palm G (2011) A Framework for Application-Oriented Design of Large-Scale Neural Networks. In Honkela, Timo and Duch, Włodzisław and Girolami, Mark and Kaski, Samuel, Artificial Neural Networks and Machine Learning – ICANN 2011. Springer Berlin Heidelberg. 6792:317–324 [doi]
Bos H, Diesmann M and Helias M (2016) [Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit. ] Public Library of Science PLOS Computational Biology. 12:1-34 [url] [doi]
Bichler O, Roclin D and Gamrat C (2013) [Design Exploration Methodology for Memristor-Based Spiking Neuromorphic Architectures with the Xnet Event-Driven Simulator. ] [pdf] [doi]
Bhalla U. (2012) Multi-compartmental Models of Neurons. Springer Netherlands:193–225 [doi]
Beyeler M., Carlson K.D., Chou TS, Dutt N. and Krichmar J.L. (2015) CARLsim 3: A user-friendly and highly optimized library for the creation of neurobiologically detailed spiking neural networks. , Neural Networks (IJCNN), 2015 International Joint Conference on. :1–8 [doi]
Bernardet U and Verschure PF.M.J. (2010) IQR: a tool for the construction of multi-level simulations of brain and behaviour. Neuroinformatics. 8:113–134 [doi]
Berger D, Borgelt C, Louis S, Morrison A and Grün S (2010) Efficient identification of assembly neurons within massively parallel spike trains. Comput Intell Neurosci. Volume 2010:439648 [doi]
Benjaminsson S and Lansner A (2012) Nexa: a scalable neural simulator with integrated analysis. Network. 23:254–271 [doi]
Belic JJ., Kumar A and Hellgren Kotaleski J (2017) [Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations. ] Public Library of Science PLOS ONE. 12:1-17 [url] [doi]
Belic JJ., Kumar A and Hellgren Kotaleski J (2017) [Interactions in the Striatal Network with Different Oscillation Frequencies. ] Springer International Publishing:129–136 [url] [doi]
Bekolay T, Bergstra J, Hunsberger E, DeWolf T, Stewart TC., Rasmussen D, Choo X, Voelker AR and Eliasmith C (2013) Nengo: a Python tool for building large-scale functional brain models. Front Neuroinform.. 7:48 [doi]
Beeman D (2013) [History of Neural Simulation Software. ] Springer Series in Computational Neuroscience. 9:33–71 [url] [doi]
Bednar JA. (2008) Understanding Neural Maps with Topographica. Brains, Minds, and Media. 3 [doi]
Bednar JA. (2009) [Topographica: Building and Analyzing Map-Level Simulations from Python, C/C++, MATLAB, NEST, or NEURON Components. ] Frontiers in Neuroinformatics. 3:8 [url] [doi]
Beata Strack KM.JKJ.C (2014) [Simulating vertical and horizontal inhibition with short-term dynamics in a multi-column multi-layer model of neocortex. ] International Journal of Neural Systems. 24:1440002 [url] [doi]
Bahuguna J, Aertsen A and Kumar A (2015) [Existence and Control of Go/No-Go Decision Transition Threshold in the Striatum. ] Public Library of Science PLoS Comput Biol. 11:e1004233 [url] [doi]
Backofen R., Borrmannand H.-G., Deck W., Dedner A., Raedt L. D, Desch K., Diesmann M., Geier M., Greiner A., Hess W. R., Honerkamp J., Jankowski S., Krossing I., Liehr A. W., Karwath A., Klofkorn R., Pesche R., Potjans T., Rottger M. C., Schmidt-Thieme L., Schneider G., Voss B., Wiebelt B., Wienemann P. and Winterer V.-H. (2007) [A Bottom-up approach to Grid-Computing at a University: the Black-Forest-Grid Initiative. ] Praxis der Informationsverarbeitung und Kommunikation. 29:81–87 [url] [doi]
Aviel Y, Pavlov E., Abeles M and Horn D (2003) [Synfire Chain in a Balanced Network. ] Neural Computation. 15:1321–1340 [url] [doi]
Avermann M, Tomm C, Mateo C, Gerstner W and Petersen CCH (2012) Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. J Neurophysiol. 107:3116–3134 [doi]
Arnoldt H, Chang S, Jahnke S, Urmersbach B, Taschenberger H and Timme M (2015) [When Less Is More: Non-monotonic Spike Sequence Processing in Neurons. ] Public Library of Science PLoS Comput Biol. 11:e1004002 [url] [doi]
Antolik J and Davison AP. (2013) [Integrated workflows for spiking neuronal network simulations. ] Front Neuroinform.. 7:34 [url] [doi]
Ando H., Karthik K. and Chakravarthy V.S. (2010) Studying the economy of energy expenditure in a large balanced spiking neuron network. , Neural Networks (IJCNN), The 2010 International Joint Conference on. :1–5 [doi]
Amir A., Datta P., Risk W.P., Cassidy A.S., Kusnitz J.A., Esser S.K., Andreopoulos A., Wong T.M., Flickner M., Alvarez-Icaza R., McQuinn E., Shaw B., Pass N. and Modha D.S. (2013) [Cognitive computing programming paradigm: A Corelet Language for composing networks of neurosynaptic cores. ]:1–10 [pdf] [doi]
Ambrosano A, Vannucci L, Albanese U, Kirtay M, Falotico E, Martinez-Canada P, Hinkel G, Kaiser J, Ulbrich S, Levi P, Morillas C, Knoll A, Gewaltig MO and Laschi C (2016) Retina Color-Opponency Based Pursuit Implemented Through Spiking Neural Networks in the Neurorobotics Platform. Springer International Publishing:16–27 [doi]
van Albada SJ, Rowley AG. D., Hopkins M, Schmidt M, Senk J, Stokes AB, Galluppi F, Lester DR., Diesmann M and Furber SB. (2016) Full-scale simulation of a cortical microcircuit on SpiNNaker. Frontiers in Neuroinformatics [doi]
van Albada SJ, Helias M and Diesmann M (2015) [Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations. ] Public Library of Science PLoS Comput Biol. 11:e1004490 [url] [doi]
van Albada S.J., Kunkel S., Morrison A. and Diesmann M. (2014) Integrating brain structure and dynamics on supercomputers. Springer International Publishing:22–32 [doi]
van Albada S, Helias M and Diesmann M (2015) [Limits to the scalability of cortical network models. ] BMC Neuroscience. 16:O1 [url] [doi]
Akam T and Kullmann DM. (2010) Oscillations and filtering networks support flexible routing of information. Neuron. 67:308–320 [doi]
Muller E, Bednar JA, Diesmann M, Gewaltig MO, Hines M and Davison AP (2015) [Python in Neuroscience. ] Frontiers in Neuroinformatics. 9:11 [url] [doi]
Potjans TC and Diesmann M (2014) [The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model. ] Cereb Cortex. 3:785–806 [url] [doi]
Mohemmed A, Schliebs S, Matsuda S and Kasabov N (2013) [Training spiking neural networks to associate spatio-temporal input–output spike patterns. ] Neurocomputing. 107:3–10 [url] [doi]
Cantarelli M, Marin B, Quintana A, Earnshaw M, Court R, Gleeson P, Dura-Bernal S, Silver R. A and Idili G (2018) [Geppetto: a reusable modular open platform for exploring neuroscience data and models. ] The Royal Society Philosophical Transactions of the Royal Society B: Biological Sciences. 373:20170380 [url] [doi]
Falotico E, Vannucci L, Ambrosano A, Albanese U, Ulbrich S, Vasquez Tieck JC, Hinkel G, Kaiser J, Peric I, Denninger O, Cauli N, Kirtay M, Roennau A, Klinker G, Von Arnim A, Guyot L, Peppicelli D, Martínez-Cañada P, Ros E, Maier P, Weber S, Huber M, Plecher D, Röhrbein F, Deser S, Roitberg A, van der Smagt P, Dillman R, Levi P, Laschi C, Knoll AC. and Gewaltig MO (2017) [Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform. ] Frontiers in Neurorobotics. 11:2 [url] [doi]
Tikidji-Hamburyan RA., Narayana V, Bozkus Z and El-Ghazawi TA. (2017) [Software for Brain Network Simulations: A Comparative Study. ] Frontiers in Neuroinformatics. 11:46 [url] [doi]
Einarsson H, Gauy MM, Lengler J and Steger A (2017) [A Model of Fast Hebbian Spike Latency Normalization. ] Frontiers Media S.A. Frontiers in Computational Neuroscience. 11:33– [url] [doi]
Eliasmith C and Trujillo O (2014) [The use and abuse of large-scale brain models. ] Current Opinion in Neurobiology. 25:1–6 [url] [doi]
Shehzad D, Bozku&x15f and Z (2016) [Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems. ] Computational Intelligence and Neuroscience. 2016:10 [url] [doi]
Liu Q, Pineda-GarcÃa G, Stromatias E, Serrano-Gotarredona T and Furber SB. (2016) Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation. Frontiers in Neuroscience. 10:496 [doi]
Cheung K, Schultz SR. and Luk W (2016) [NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors. ] Frontiers in Neuroscience. 9:516 [url] [doi]
Voges N. and Perrinet L. (2012) [Complex dynamics in recurrent cortical networks based on spatially realistic connectivities. ] Front Comput Neurosci. 6:41 [url] [doi]
Vlachos I, Aertsen A and Kumar A (2012) [Beyond statistical significance: implications of network structure on neuronal activity. ] PLoS Comput Biol. 8:e1002311 [url] [doi]
Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM., Diesmann M, Morrison A, Goodman PH., Harris JFC, Zirpe M, Natschläger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP., El Boustani S and Destexhe A (2007) [Simulation of networks of spiking neurons: a review of tools and strategies. ] J. Comput. Neurosci.. 23:349–398 [url] [doi]
Davison AP., Brüderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L and Yger P (2008) PyNN: A Common Interface for Neuronal Network Simulators. Front. Neuroinform.. 2:11 [file] [doi]
Kobayashi R, Tsubo Y and Shinomoto S (2009) Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Front Comput Neurosci. 3:9 [doi]
Scholze S, Schiefer S, Partzsch J, Hartmann S, Mayr CG, Höppner S, Eisenreich H, Henker S, Vogginger B and Schüffny R (2011) VLSI Implementation of a 2.8 Gevent/s Packet-Based AER Interface with Routing and Event Sorting Functionality. Front Neurosci. 5:117 [doi]
Kamali Sarvestani I, Lindahl M, Hellgren-Kotaleski J and Ekeberg Ö (2011) [The Arbitration–Extension Hypothesis: A Hierarchical Interpretation of the Functional Organization of the Basal Ganglia. ] Frontiers in Computational Neuroscience. 5:13 [url] [doi]
Hines M, Kumar S and Schürmann F (2011) [Comparison of neuronal spike exchange methods on a Blue Gene/P supercomputer. ] Frontiers in Computational Neuroscience. 5:49 [url] [doi]
Bustos OEH and Bustos LH (2011) [Efect of the Kv3.1 potassium conductance in the firing frequency and firing rate of an unmyelinated axon. ] Salud, Barranquilla. 27:2 [pdf]
Bustos OEH and Bustos LH (2011) [Relationship between sodium and potassium conductances Hodgkin and Huxley style and their effect in propagation of the action potential at 40°C. ] Revista Científica Salud Uninorte; Salud Uninorte. Barranquilla. 27:223–225 [pdf]
Eppler J. M., Kupper R., Plesser H. E. and Diesmann M. (2009) A testsuite for a neural simulation engine. , Frontiers in Neuroinformatics. Conference Abstract: 2nd INCF Congress of Neuroinformatics. International Neuroinformatics Coordinating Facility [doi]
Kunkel S, Eppler JM, Plesser HE, Gewaltig MO, Diesmann M and Morrison A (2010) NEST: Science-driven development of neuronal network simulation software. , Frontiers in Neuroscience. Conference Abstract: Neuroinformatics 2010. [doi]
Gewaltig MO, Diesmann M and Aertsen A (2001) [Cortical synfire-activity: Configuration space and survival probability. ] Elsevier BV Neurocomputing. 38–40:621–626 [url] [doi]
Gewaltig MO, Diesmann M and Aertsen A (2001) [Propagation of cortical synfire activity: survival probability in single trials and stability in the mean. ] Elsevier BV Neural Networks. 14:657–673 [url] [doi]
Diesmann M, Gewaltig MO, Rotter S and Aertsen A (2001) [State space analysis of synchronous spiking in cortical neural networks. ] Elsevier BV Neurocomputing. 38–40:565–571 [url] [doi]
Tetzlaff T, Geisel T and Diesmann M (2002) [The ground state of cortical feed-forward networks. ] Elsevier BV Neurocomputing. 44–46:673–678 [url] [doi]
Ehrlich M, Wendt K, Zühl L, Schüffny R, Brüderle D, Müller E and Vogginger B (2010) A Software Framework for Mapping Neural Networks to a Wafer-scale Neuromorphic Hardware System. , ANNIIP. :43–52

739 found.