NEST Simulator

576 found.

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 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 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 doi
Gruel A, Hareb D, Grimaldi A, Martinet J, Perrinet L, Linares-Barranco B and Serrano-Gotarredona T (2023) Stakes of neuromorphic foveation: a promising future for embedded event cameras. Springer Science and Business Media LLC Biological Cybernetics. 117:389–406 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 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 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 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 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 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 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 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 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 doi
Wang Z, Yu N and Essaf F (2023) A soma-synapses neuron model and FPGA implementation. Wiley Concurrency and Computation: Practice and Experience 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 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 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 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 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 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 doi
Giacopelli G, Migliore M and Tegolo D (2023) NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation. MDPI AG Sensors. 23:4598 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 doi
Shirsavar SR, Vahabie AH and Dehaqani MRA. (2023) Models developed for spiking neural networks. Elsevier BV MethodsX. 10:102157 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 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 doi
Wei W and Wang X (2022) Ion-specific effects in confined nanochannels and neural network. Wiley Aggregate. 4:e302 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 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 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 doi
D'Angelo E and Jirsa V (2022) The quest for multiscale brain modeling. Elsevier BV Trends in Neurosciences. 45:777–790 doi
Griffiths JD., Shen K and Gleeson P (2022) Editorial: Neuroinformatics of large-scale brain modelling. Frontiers Media SA Frontiers in Neuroinformatics. 16:1043732 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 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 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 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 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 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 doi
Yamazaki K, Vo-Ho VK, Bulsara D and Le N (2022) Spiking Neural Networks and Their Applications: A Review. Brain Sciences. 12:863 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 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 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 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 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 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 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 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 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 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 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 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 doi
Peres L and Rhodes O (2022) Parallelization of Neural Processing on Neuromorphic Hardware. Frontiers in Neuroscience. 16:867027 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 doi
Ostrau C, Klarhorst C, Thies M and Rückert U (2022) Benchmarking Neuromorphic Hardware and Its Energy Expenditure. Frontiers in Neuroscience. 16:873935 doi
Osborne H and de Kamps M (2022) A numerical population density technique for N-dimensional neuron models. Frontiers in Neuroinformatics. 16:883796 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 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 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 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 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 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 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 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 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 doi
Kon M and Francis G (2022) Cortical circuits for top-down control of perceptual grouping. Neural Networks. 151:190-210 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 doi
Huang C, Zeldenrust F and Celikel T (2022) Cortical Representation of Touch in Silico. Neuroinformatics:1-27 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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. Elsevier BV Neurocomputing. 449:422–434 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 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 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 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 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 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 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 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 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 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 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 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 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 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 doi
Yin Y (2021) RANDOM NEURAL NETWORK METHODS AND DEEP LEARNING. Cambridge University Press (CUP) Probability in the Engineering and Informational Sciences:1–31 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 Sciences Publications, Ltd eLife. 10:e60936 doi
Wardak A and Gong P (2021) Fractional diffusion theory of balanced heterogeneous neural networks. American Physical Society (APS) Physical Review Research. 3:013083 doi
Yin Y (2021) RANDOM NEURAL NETWORK METHODS AND DEEP LEARNING. Probability in the Engineering and Informational Sciences. 35:6-36 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 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 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
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
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 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 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 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 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
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 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 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 doi
Knight JC., Komissarov A and Nowotny T (2021) PyGeNN: A Python Library for GPU-Enhanced Neural Networks. Frontiers in Neuroinformatics. 15:659005 doi
Jordan J, Schmidt M, Senn W and Petrovici MA (2021) Evolving interpretable plasticity for spiking networks. eLife. 10:e66273 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 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 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
Cakan C, Jajcay N and Obermayer K (2021) neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling. Cognitive Computation:1-21 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 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. Public Library of Science (PLOS) PLOS Computational Biology. 16:e1008386 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 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 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 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 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 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 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 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 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 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 doi
Huyck CR. (2020) A neural cognitive architecture. Elsevier BV Cognitive Systems Research. 59:171–178 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 doi
LaTorre A, Kwong MT, Garcı́a-Grajales JA., Shi R, Jérusalem A and Peña JḾa (2020) Model calibration using a parallel differential evolution algorithm in computational neuroscience: Simulation of stretch induced nerve deficit. Elsevier BV Journal of Computational Science. 39:101053 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 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 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 doi
Rhodes O, Peres L, Rowley AG. D., Gait A, Plana LA., Brenninkmeijer C and Furber SB. (2020) Real-time cortical simulation on neuromorphic hardware. Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences. 378:20190160 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 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 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 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 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 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 doi
Lee JH (2020) Biologically plausible mechanisms underlying motor response correction during reward-based decision-making. Neurocomputing. 412:416-425 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 doi
Kim CM, Egert U and Kumar A (2020) Dynamics of multiple interacting excitatory and inhibitory populations with delays. Physical Review E. 102:022308 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 doi
Hazan H, Saunders DJ., Sanghavi DT., Siegelmann H and Kozma R (2020) Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data. Annals of Mathematics and Artificial Intelligence. 88:1237-1260 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 doi
DeWolf T, Jaworski P and Eliasmith C (2020) Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics. Frontiers in Neurorobotics. 14:568359 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 doi
Crook SM., Davison AP., McDougal RA. and Plesser HE (2020) Editorial: Reproducibility and Rigour in Computational Neuroscience. Frontiers in Neuroinformatics. 14:23 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 doi
Stimberg M, Brette R and Goodman DF (2019) Brian 2, an intuitive and efficient neural simulator. eLife Sciences Publications, Ltd eLife. 8:e47314 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 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 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 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 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 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 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 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 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 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 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 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 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 doi
Rasmussen D (2019) NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods. Springer Nature Neuroinformatics. 17:611–628 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 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 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 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 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 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 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 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 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 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 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 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 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 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 doi
Huyck C and Mitchell I (2018) CABots and Other Neural Agents. Frontiers Media SA Frontiers in Neurorobotics. 12:79 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 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 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 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 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 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 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 doi
Scheffer LK. (2018) Analysis Tools for Large Connectomes. Frontiers Media SA Frontiers in Neural Circuits. 12:85 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 doi
Antolík J and Davison AP. (2018) Arkheia: Data Management and Communication for Open Computational Neuroscience. Frontiers Media SA Frontiers in Neuroinformatics. 12:6 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 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 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 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 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 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 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 doi
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 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 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 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 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 doi
Lee JH (2017) Disrupted cholinergic modulation can underlie abnormal gamma rhythms in schizophrenia and auditory hallucination. Journal of Computational Neuroscience. 43:173–187 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– 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 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– 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– 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 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 doi
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 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 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 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 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 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 doi
Vannucci L, Falotico E and Laschi C (2017) Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model. Frontiers in Neuroscience. 11:341 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 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 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 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 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 doi
Lee JH and Mihalas S (2017) Visual processing mode switching regulated by VIP cells. Scientific Reports. 7:1843 doi
Ippen T, Eppler JM., Plesser HE and Diesmann M (2017) Constructing neuronal network models in massively parallel environments. Front. Neuroinform.. 11:30 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 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 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 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 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 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 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 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
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
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 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
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
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 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 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
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 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 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 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 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 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 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 doi
Westoe J and May PJ.C. (2016) Capturing contextual effects in spectro-temporal receptive fields. Hearing Research. 339:195 - 210 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
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 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
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 doi
Ray S, Chintaluri C, Bhalla US. and Wójcik DK. (2016) NSDF: Neuroscience Simulation Data Format. Neuroinformatics. 14:147–167 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
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 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
Sukhinin DI., Engel AK., Manger P and Hilgetag CC. (2016) Building the Ferretome. Frontiers in Neuroinformatics. 10:16 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
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 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 doi
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
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 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
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 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 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 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
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 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
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 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 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 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 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 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 doi
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 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:- 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
Ray S, Chintaluri C, Bhalla US. and Wójcik DK. (2015) NSDF: Neuroscience Simulation Data Format. Neuroinformatics:1–21 doi
Muller E, Bednar JA, Diesmann M, Gewaltig MO, Hines M and Davison AP (2015) Python in Neuroscience. Frontiers in Neuroinformatics. 9:11 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
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 doi
Huyck C, Evans C and Mitchell I (2015) A comparison of simple agents implemented in simulated neurons. Biologically Inspired Cognitive Architectures:- 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:- 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
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 doi
van Albada S, Helias M and Diesmann M (2015) Limits to the scalability of cortical network models. BMC Neuroscience. 16:O1 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 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
Vitay J, Dinkelbach HÜ and Hamker FH (2015) ANNarchy: a code generation approach to neural simulations on parallel hardware. Frontiers in Neuroinformatics. 9:19 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
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 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 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
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 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 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 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 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
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 doi
Zaytsev YV. and Morrison A (2014) CyNEST: a maintainable Cython-based interface for the NEST simulator. Frontiers in Neuroinformatics. 8:23 doi
Wazny M and Wojcik GM. (2014) Shifting spatial attention—Numerical model of Posner experiment. Neurocomputing. 135:139–144 doi
Sousa M and Aguiar P (2014) Building, simulating and visualizing large spiking neural networks with NeuralSyns. Neurocomputing. 123:280–372 doi
Segundo J.Guzman ASCSH (2014) Stimfit: quantifying electrophysiological data with Python. Front Neuroinform. 8:16 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 doi
Philip J. Tully MH. H and Lansner A (2014) Synaptic and nonsynaptic plasticity approximating probabilistic inference. Frontiers in Synaptic Neuroscience. 6:8 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 doi
Djurfeldt M, Davison AP. and Eppler JM. (2014) Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Front Neuroinform. 8:43 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 doi
Stimberg M., Goodman D., Benichoux V. and Brette1 R. (2014) Equation-oriented specification of neural models for simulations. Front Neuroinform. 8:6 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
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 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
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
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
Gewaltig MO and Cannon R (2014) Current Practice in Software Development for Computational Neuroscience and How to Improve It. PLOS Computational Biology. 10:e1003376 doi
Eliasmith C and Trujillo O (2014) The use and abuse of large-scale brain models. Current Opinion in Neurobiology. 25:1–6 doi
Chun-Wei Yuan LKBG and Leibold C (2014) Neuronal Adaptation Translates Stimulus Gaps into a Population Code. PLOS One. 9:e95705 doi
Chen W and Schutter ED (2014) Python-based geometry preparation and simulation visualization toolkits for STEPS. Frontiers in Neuroinformatics. 8:37 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
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 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
Duarte RCF and Morrison A (2014) Dynamic stability of sequential stimulus representations in adapting neuronal networks. Frontiers in Computational Neuroscience. 8:124 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 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 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 doi
Zaytsev YV. and Morrison A (2013) Increasing quality and managing complexity in neuroinformatics software development with continuous integration. Front. Neuroinform.. 6:31 doi
Yger P and Harris KD. (2013) The Convallis Rule for Unsupervised Learning in Cortical Networks. PLOS Comput Bio. 9:10 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 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 doi
Vlachos I, Zaytsev YV., Spreizer S, Aertsen A and Kumar A (2013) Neural system prediction and identification challenge. Front Neuroinform.. 7:43 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 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
Schwartz MO (2013) Reproducing Biologically Realistic Regimes on a Highly-Accelerated Neuromorphic Hardware System. doi
Schutter E (2013) Collaborative Modeling in Neuroscience: Time to Go Open Model?. Springer-Verlag Neuroinformatics. 11:135–136 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 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 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
Parekh R and Ascoli GA. (2013) Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience. Neuron. 77:1017–1038 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 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
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 doi
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
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
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
Lengler J, Jug F and Steger A (2013) Reliable Neuronal Systems: The Importance of Heterogeneity. PLOS One. 8:12:e80694 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
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 doi
Jahnke S, Memmesheimer RM and Timme M (2013) Propagating synchrony in feed-forward networks. Front Comput Neurosci. 7:153 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 doi
Hull MJ. and Willshaw DJ. (2013) morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python. Front Neuroinform.. 7:47 doi
Holt AB. and Netoff TI. (2013) Computational modeling of epilepsy for an experimental neurologist. Experimental Neurology. 244:75–86 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
Helias M, Tetzlaff T and Diesmann M (2013) Echoes in correlated neural systems. New Journal of Physics. 15:023002 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
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
Ferreiroa R and Sánchez E (2013) Functional properties of a realistic model of dLGN. Neurocomputing. 114:36–44 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 doi
Cohen MX. and Gulbinaite R (2013) Five methodological challenges in cognitive electrophysiology. NeuroImage:- 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 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
Antolik J and Davison AP. (2013) Integrated workflows for spiking neuronal network simulations. Front Neuroinform.. 7:34 doi
Jahnke S, Timme M and Memmesheimer RM (2012) Guiding Synchrony through Random Networks. American Physical Society Phys. Rev. X. 2:041016 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
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
Kriener B (2012) How synaptic weights determine stability of synchrony in networks of pulse-coupled excitatory and inhibitory oscillators. Chaos. 22:033143 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
Tetzlaff T, Helias M, Einevoll GT and Diesmann M (2012) Decorrelation of neural-network activity by inhibitory feedback. PLoS Comput Biol. 8:e1002596 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
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
Schmitt O and Eipert P (2012) neuroVIISAS: approaching multiscale simulation of the rat connectome. Neuroinformatics. 10:243–267 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
Deger M, Helias M, Boucsein C and Rotter S (2012) Statistical properties of superimposed stationary spike trains. J Comput Neurosci. 32:443–463 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
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
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
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 doi
Vlachos I, Aertsen A and Kumar A (2012) Beyond statistical significance: implications of network structure on neuronal activity. PLoS Comput Biol. 8:e1002311 doi
Yuan CW and Leibold C (2012) Recurrent coupling improves discrimination of temporal spike patterns. Front Comput Neurosci. 6:25 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. (2012) Complex dynamics in recurrent cortical networks based on spatially realistic connectivities. Front Comput Neurosci. 6:41 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
Rougier NP. and Fix J (2012) DANA: distributed numerical and adaptive modelling framework. Network. 23:237–253 doi
Ren Q, Kolwankar KM., Areejit Samal and Jost J (2012) Hopf bifurcation in the evolution of STDP-driven networks. Physical review. 86:13 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
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
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 doi
Mattioni M, Cohen U and Novère NL (2012) Neuronvisio: A Graphical User Interface with 3D Capabilities for NEURON. Front Neuroinform. 6:20 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 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
Kamps Mde (2012) Towards truly human-level intelligence in artificial applications. Cognitive Systems Research. 14:1–9 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
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
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
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] 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
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
Clewley R (2012) Hybrid Models and Biological Model Reduction with PyDSTool. PLoS Comput Biol. 8:e1002628 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 doi
Benjaminsson S and Lansner A (2012) Nexa: a scalable neural simulator with integrated analysis. Network. 23:254–271 doi
Baptista D'io and Morgado-Dias F (2012) A survey of artificial neural network training tools. Neural Computating and Applications. 23:609–615 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
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
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
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
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 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
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
Brette R and Goodman DFM (2011) Vectorized algorithms for spiking neural network simulation. Neural Comput. 23:1503–1535 doi
Pernice V, Staude B, Cardanobile S and Rotter S (2011) How structure determines correlations in neuronal networks. PLoS Comput Biol. 7:e1002059 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
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
Yim M.Y., Aertsen A. and Kumar A. (2011) Significance of input correlations in striatal function. PLoS Comput Biol. 7:e1002254 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
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 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
Touboul J (2011) On the Simulation of Nonlinear Bidimensional Spiking Neuron Models. Neural Comput. 23:1704–1742 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
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
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
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 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
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 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
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 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
Kolwankar KM., Ren Q, Samal A and Jost J (2011) Learning and structure of neuronal networks. Springer-Verlag Pramana. 77:817–826 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
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 doi
Hugo Cornelis AD. CJM. B (2011) A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture. PLOS One. 7:e28956 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 doi
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
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, Grytskyy D, Tetzlaff T and Diesmann M (2011) Model-invariant features of correlations in recurrent networks. Frontiers in Computational Neuroscience. 10:3389 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., 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 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
Cornelis H, Rodriguez AL., Coop AD. and Bower JM. (2011) Python as a Federation Tool for GENESIS 3.0. PLOS One. 7:1 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
Goodman DFM (2010) Code generation: a strategy for neural network simulators. Neuroinformatics. 8:183–196 doi
Akam T and Kullmann DM. (2010) Oscillations and filtering networks support flexible routing of information. Neuron. 67:308–320 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
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
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
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
Voges N and Perrinet L (2010) Phase space analysis of networks based on biologically realistic parameters. J Physiol Paris. 104:51–60 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 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
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
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
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 doi
Linaro D., Poggi T. and Storace M. (2010) Experimental bifurcation diagram of a circuit-implemented neuron model. Physics Letters A. 374:4589–4593 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
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, 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
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
Gollo LL., Mirasso C and Villa AE.P. (2010) Dynamic control for synchronization of separated cortical areas through thalamic relay. Neuroimage. 52:947–955 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
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
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
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
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
Goodman DFM and Brette R (2009) The brian simulator. Front Neurosci. 3:192–197 doi
Nordlie E, Gewaltig MO and Plesser HE (2009) Towards reproducible descriptions of neuronal network models. PLoS Comput Biol. 5:e1000456 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
Plesser HE and Diesmann M (2009) Simplicity and efficiency of integrate-and-fire neuron models. Neural Comput. 21:353–359 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
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
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
Eppler J (2009) A Python interface to NEST. The Neuromorphic Engineer doi
Einevoll GT. (2009) Sharing with Python. Front Neurosci.. 3:334–335 doi
Eichner H, Klug T and Borst A (2009) Neural simulations on multi-core architectures. Front Neuroinform. 3:21 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 doi
Davison AP., Hines ML. and Muller E (2009) Trends in Programming Languages for Neuroscience Simulations. Front Neurosci.. 3:374–380 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
Bednar JA. (2009) Topographica: Building and Analyzing Map-Level Simulations from Python, C/C++, MATLAB, NEST, or NEURON Components. Frontiers in Neuroinformatics. 3:8 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
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
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
Morrison A, Diesmann M and Gerstner W (2008) Phenomenological models of synaptic plasticity based on spike timing. Biol Cybern. 98:459–478 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
Schutter ED (2008) Why are computational neuroscience and systems biology so separate?. PLoS Comput Biol. 4:e1000078 doi
Kumar A, Schrader S, Aertsen A and Rotter S (2008) The high-conductance state of cortical networks. Neural Comput. 20:1–43 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
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 doi
Meier R, Egert U, Aertsen A and Nawrot MP. (2008) FIND — A unified framework for neural data analysis. Neural Networks. 21:1085–1093 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 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
Morrison A and Diesmann M (2008) Maintaining causality in discrete time neuronal network simulations. Springer-Verlag Berlin and Heidelberg GmbH & Co. KG:267-278 doi
Goodman D and Brette R (2008) Brian: a simulator for spiking neural networks in python. Front Neuroinform. 2:5 doi
Goedeke S and Diesmann M (2008) The mechanism of synchronization in feed-forward neuronal networks. New Journal of Physics. 10:015007 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
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., 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
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 doi
Bednar JA. (2008) Understanding Neural Maps with Topographica. Brains, Minds, and Media. 3 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 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
Morrison A, Aertsen A and Diesmann M (2007) Spike-Timing-Dependent Plasticity in Balanced Random Networks. MIT Press - Journals Neural Computation. 19:1437–1467 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
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 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 doi
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 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 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 doi
Gewaltig MO and Diesmann M (2007) NEST:NEural Simulation Tool. Scholarpedia. 2:1430
Cornelis H and Schutter ED (2007) Neurospaces:Towards automated model partitioning for parallel computers. Neurocomputing. 70:2117–2121 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
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 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 doi
Mayor J and Gerstner W (2005) Noise-enhanced computation in a model of a cortical column. Cognitive Neurosci. 16:1237–1240
Kupper R, Gewaltig MO, Körner U and Körner E (2005) Spike-latency codes and the effect of saccades. Neurocomputing. 65–66:189–194 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 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 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
Aviel Y, Pavlov E., Abeles M and Horn D (2003) Synfire Chain in a Balanced Network. Neural Computation. 15:1321–1340 doi
Gewaltig MO, Richter A and Kupper R (2002) BLISS: towards the simulation of brain-like systems. Neurocomputing. 44-46:805–810 doi
Tetzlaff T, Geisel T and Diesmann M (2002) The ground state of cortical feed-forward networks. Elsevier BV Neurocomputing. 44–46:673–678 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 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 doi
Gewaltig MO, Diesmann M and Aertsen A (2001) Cortical synfire-activity: Configuration space and survival probability. Elsevier BV Neurocomputing. 38–40:621–626 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
Diesmann M, Gewaltig MO and Aertsen A (1999) Stable propagation of synchronous spiking in cortical neural networks. Nature. 402:529–533 doi
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

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