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Deepcopy problem with connections
by Xavier Otazu 01 Mar '23

01 Mar '23

Hello, For the below code I get an error related to __cinit__ (Cython) when performing a deepcopy that includes a nest.spatial specification. I am using NEST 3.3 and Python 3.8.12 Best, Xavier ---- import nest import copy my_dict = {'rule': 'pairwise_bernoulli', 'p': nest.spatial_distributions.gaussian(nest.spatial.distance, std=1.0), 'mask': {'circular': {'radius': 4.}}} new_dict = copy.deepcopy(my_dict) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib64/python3.8/copy.py", line 146, in deepcopy y = copier(x, memo) File "/usr/lib64/python3.8/copy.py", line 230, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib64/python3.8/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/usr/lib64/python3.8/copy.py", line 270, in _reconstruct state = deepcopy(state, memo) File "/usr/lib64/python3.8/copy.py", line 146, in deepcopy y = copier(x, memo) File "/usr/lib64/python3.8/copy.py", line 230, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib64/python3.8/copy.py", line 161, in deepcopy rv = reductor(4) File "stringsource", line 2, in pynestkernel.SLIDatum.__reduce_cython__ TypeError: no default __reduce__ due to non-trivial __cinit__

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Free NESTML and NEST Desktop Workshop (27.03.2023)
by Jens Bruchertseifer 27 Feb '23

27 Feb '23

Hello everyone, we are pleased to announce that NESTML and NEST Desktop will be represented at the satellite training sessions (https://flagship.kip.uni-heidelberg.de/jss/HBPm?mI=252&m=showAgenda&showAbs…) as part of the HBP Summit 2023 in Marseille! These sessions will take place on *27 March 2023*. Participation is free of charge, but a registration is mandatory. Please note that only a limited number of participants can register - at the moment there are still places available! The registration page is https://flagship.kip.uni-heidelberg.de/jss/HBPm?meetingID=252. You might need to create a free EBRAINS account for that, if you do not have one yet. The abstract of the workshop can be found below, as well as on the conference page. We also have a dedicated page for this tutorial with detailed information on the topics covered (https://clinssen.github.io/HBP-summit-2023-workshop/). Catering will be provided on site. We are looking forward to meeting you at the workshop! On behalf of the tutorial organizers, Jens Bruchertseifer PS: Please have also a look at the other exciting topics at the training session! ;) ----- License to Spike - A NEST Desktop and NESTML Workshop NEST is an established, open-source simulator for spiking neuronal networks, which can capture a high degree of detail of biological network structures while retaining high performance and scalability from laptops to HPC [1]. This tutorial provides hands-on experience in building and simulating neuron, synapse, and network models. It introduces several tools and front-ends to implement modeling ideas most efficiently. Participants do not have to install software as all tools can be accessed via the cloud. First, we look at NEST Desktop [2], a web-based graphical user interface (GUI), which allows the exploration of essential concepts in computational neuroscience without the need to learn a programming language. This advances both the quality and speed of teaching in computational neuroscience. To get acquainted with the GUI, we will create and analyze a balanced two-population network. In the second half of the session, we will create a new, custom neuron model that extends the capabilities of NEST Simulator by introducing new mechanisms, such as an active spiking dendritic compartment. NESTML [3] makes it quick and easy it is to implement and simulate model variants. A neuronal plasticity rule is then introduced, which allows a network to be trained by means of reinforcement learning. This is accomplished by combinating a typical spike-timing dependent plasticity learning rule with a global neuromodulatory dopamine signal. We will use the new learning rule to train a stimulus preference in the balanced network. Citations [1] https://nest-simulator.readthedocs.org/ [2] https://nest-desktop.readthedocs.org/ [3] https://nestml.readthedocs.org/

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NEST Conference 2023 – Save the date & registration
by Hans Ekkehard Plesser 27 Feb '23

27 Feb '23

Dear Colleagues, The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the NEST Conference 2023. The NEST Conference provides an opportunity for the NEST Community to meet, exchange success stories, swap advice, learn about current developments in and around NEST spiking network simulation and its application. We particularly encourage young scientists to participate in the conference! This year's conference will take place as a virtual event on 15-16 June 2023. Register now! For more information please visit the conference website https://nest-simulator.org/conference We are looking forward to seeing you all in June! Hans Ekkehard Plesser and colleagues [Image] -- Prof. Dr. Hans Ekkehard Plesser Head, Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway Phone +47 6723 1560 Email hans.ekkehard.plesser(a)nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no> Home http://arken.nmbu.no/~plesser

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Open NEST Developer Video Conference Monday Feb 27 2023 11:30 CET (UTC+1)
by Dennis Terhorst 27 Feb '23

27 Feb '23

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference, today Monday February 27, 11.30-12.30 CET (UTC+1). * Special topic for the in-depth today will be the introduction to "NEAT" a Neural Analysis Toolkit by @WillemWybo. * Additionally, @heplesser will shed more light on performance optimizations described in #2617: Integrating "deliver events first". Feel free to join the meeting also if it's just to bring your own quick questions for direct discussion in the in-depth section. As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing work in the team and cross-cutting points that need discussion among the teams. The remainder of the meeting we would go into a more in-depth discussion of topics that came up on the mailing list or that are suggested by the teams. Agenda * Welcome * Review of NEST User Mailing List * Project team round * In-depth discussion The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2023-02-27-Open-NEST-Developer-… Looking forward to seeing you! Cheers, Dennis Terhorst ------------------ Log-in information ------------------ We use a virtual conference room provided by DFN (Deutsches Forschungsnetz). You can use the web client to connect. We however encourage everyone to use a headset for better audio quality or even a proper video conferencing system (see below) or software when available. Web client * Visit https://conf.dfn.de/webapp/conference/97938800 * Enter your name and allow your browser to use camera and microphone * The conference does not need a PIN to join, just click join and you're in. In case you see a dfnconf logo and the phrase "Auf den Meetingveranstalter warten", just be patient, the meeting host needs to join first (a voice will tell you). VC system/software How to log in with a video conferencing system, depends on you VC system or software. - Using the H.323 protocol (eg Polycom): vc.dfn.net##97938800 or 194.95.240.2##97938800 - Using the SIP protocol:97938800@vc.dfn.de - By telephone: +49-30-200-97938800 For those who do not have a video conference system or suitable software, Polycom provides a pretty good free app for iOS and Android, so you can join from your tablet (Polycom RealPresence Mobile, available from AppStore/PlayStore). Note that firewalls may interfere with videoconferencing in various and sometimes confusing ways. For more technical information on logging in from various VC systems, please see http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4

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RunManager and STDP connections
by Xavier Otazu 27 Feb '23

27 Feb '23

Hello, In my model I am using STDP synapse connections. When running simulations with nest.Simulate(), everything seems to work fine. When using nest.Run() inside a "with RunManager():" execution is *MUCH* faster, but STDP connections are not updated. Is it a correct behaviour? Thanks a lot in advance, Xavier

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how to record local field potential from the network
by 王梓瑞 21 Feb '23

21 Feb '23

Dear nest community, Is there anyone know how to record local field potential from the network since I only found spike/weight recorder in NEST. Thank you. Best, Zirui

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Collect the calcium concentration of the neuron without structural plasticity
by Maryada Maryada 18 Feb '23

18 Feb '23

Dear NEST Users, I would like to "record" or read calcium concentration of the neurons (let's say aeif_cond_exp). For now Ca variable is 0 before and after I simulate, however the neuron spikes. Is there any neuron model that supports this, or do I need to edit the nestml of the desired model to achieve this. If so, a sample code snippet would be helpful. -- Thanks and Regards *Maryada*

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Open NEST Developer Video Conference Monday Feb 13 2023 11:30 CET (UTC+1)
by Dennis Terhorst 13 Feb '23

13 Feb '23

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference, today Monday February 13, 11.30-12.30 CET (UTC+1). Feel free to join the meeting also if it's just to bring your own quick questions for direct discussion in the in-depth section. As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing work in the team and cross-cutting points that need discussion among the teams. The remainder of the meeting we would go into a more in-depth discussion of topics that came up on the mailing list or that are suggested by the teams. Agenda * Welcome * Review of NEST User Mailing List * Project team round * In-depth discussion The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2023-02-13-Open-NEST-Developer-… Looking forward to seeing you! Cheers, Dennis Terhorst ------------------ Log-in information ------------------ We use a virtual conference room provided by DFN (Deutsches Forschungsnetz). You can use the web client to connect. We however encourage everyone to use a headset for better audio quality or even a proper video conferencing system (see below) or software when available. Web client * Visit https://conf.dfn.de/webapp/conference/97938800 * Enter your name and allow your browser to use camera and microphone * The conference does not need a PIN to join, just click join and you're in. In case you see a dfnconf logo and the phrase "Auf den Meetingveranstalter warten", just be patient, the meeting host needs to join first (a voice will tell you). VC system/software How to log in with a video conferencing system, depends on you VC system or software. - Using the H.323 protocol (eg Polycom): vc.dfn.net##97938800 or 194.95.240.2##97938800 - Using the SIP protocol:97938800@vc.dfn.de - By telephone: +49-30-200-97938800 For those who do not have a video conference system or suitable software, Polycom provides a pretty good free app for iOS and Android, so you can join from your tablet (Polycom RealPresence Mobile, available from AppStore/PlayStore). Note that firewalls may interfere with videoconferencing in various and sometimes confusing ways. For more technical information on logging in from various VC systems, please see http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4

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Fastest way to transfer dense connectivity data in a parallel simulation?
by Robin Gilbert De Schepper 01 Feb '23

01 Feb '23

Hi all! In the world of biophysical detail, it's commonplace that the connectome is generated with algorithms that specify connections as dense tabular data, with each row specifying a synaptic location on a cell pair (SONATA for example). A) In NEST I can't really find the opportunity to fit this data into any of the connection rules: I want to specify pairwise connections from the multiset A to multiset B. - Is this possible with `pairwise bernoulli`, or do the inputs have to be strict sets? - The probability step is superfluous, can it be skipped? B) Then there's the fact that NEST parallelizes transparently, but since this data was generated in parallel by tiling the biological volume, I have neatly fragmented data already available on each node in the distributed cluster. It would be such a waste to communicate all the data to each node, for NEST to communicate and distribute them back another way. The data is too big to allgather and fit into memory of any single node. Not only is this a lot of overhead to implement, but NEST will throw away all but `1 / Nnodes` of the data on each node again, leaving me with a reshuffled version of my starting data. Is there a way to bypass the transparency and to imperatively declare the cells and connections on each machine? -- Robin De Schepper, MSc (they/them) Department of Brain and Behavioral Sciences Unit of Neurophysiology University of Pavia, Italy Via Forlanini 6, 27100 Pavia - Italy Tel: (+39) 038298-7607 http://www-5.unipv.it/dangelo/ Interested in large scale network modelling? Discover our framework <https://bsb.readthedocs.io/en/latest/>: <https://github.com/dbbs-lab/bsb>

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Re: Update: Ablating and executing a small subset of the Allen V1 model (layer23) using cpp intermediate objects where possible.
by Nicolai Haug 31 Jan '23

31 Jan '23

Hi Russell, Being able to easily build and simulate SONATA networks with NEST is hopefully imminent. We currently have a pull request under review which will add native NEST support for networks described by the SONATA format, see https://github.com/nest/nest-simulator/pull/2595. With our implementation, 64 GB of memory is more than sufficient for building and simulating the entire point neuron version of the Allen V1 model. Regarding a downscaled version of the V1 model, this cannot be achieved solely through the JSON configuration file for this particular model. This is due to all layers (V1 nodes) and associated edges being contained in single HDF5 files. In order to use a downscaled V1 model in the context of the upcoming NEST functionality, you would actually need to construct a new set of SONATA files that contain just the nodes and associated edges you are interested in. That is, you have to parse each nodes and edges .h5 and .csv file, extract the relevant entries, create new files and store the relevant entries in correctly named datasets and in the correct order. Unfortunately, I do not know of any examples that can guide you in this endeavour. You could try to reach out to the SONATA developers here https://github.com/AllenInstitute/sonata. They might have some ideas or even readily available examples. Hope this helps! Best regards, Nicolai

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