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Inhibitory plasticity with current based neurons
by Julia Gallinaro 11 May '20

11 May '20

Dear all, I would like to simulate a network of current based LIF ('iaf_psc_delta') with inhibitory plasticity. I tried using the model vogels_sprekeler_synapse, but the resulting weights are positive (and they increase when firing rate is high). I guess it is because when used with conductance based neurons, the resulting weights would be multiplied by a gI<0? And with current based neurons, this is not the case. The weight is directly used as the synaptic weight. Do I understand it right? Is there a way to implement inhibitory plasticity using the vogels_sprekeler_synapse and current based LIF? Best, Júlia

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Open NEST Developer Video Conference Monday 27 April 2020 11:30 CEST (UTC+2)
by Dennis Terhorst 11 May '20

11 May '20

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference on Monday 11 May, 11.30-12.30 CEST (UTC+2). 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 suggested from 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/2020-05-11-Open-NEST-Developer-… Looking forward to seeing you soon! best, 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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Spike Train Frequencies Inspection
by Benedikt Feldotto 08 May '20

08 May '20

Dear all, I am working with frequency encoded spike trains and therefore like to observe the changing spike frequencies between different individual connections in my network (if possible in real time). While I can record the spike events with time using 'spike_detector' units, I couldn't find a direct method to calculate the frequency. Is there any method implemented or a preferred way to calculate frequencies from spike trains over time or would this calculation be up to the user? I am also thinking about using an indirect way by attaching a leaky integrate and fire neuron with infinite threshold. Thanks! Best, Benedikt -- Benedikt Feldotto M.Sc. Research Assistant Human Brain Project - Neurorobotics Technical University of Munich Department of Informatics Chair of Robotics, Artificial Intelligence and Real-Time Systems Room HB 2.02.20 Parkring 13 D-85748 Garching b. München Tel.: +49 89 289 17628 Mail: feldotto(a)in.tum.de https://www6.in.tum.de/en/people/benedikt-feldotto-msc/ www.neurorobotics.net

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Control over the synaptic weight and synaptic decay time
by Nosratullah M 08 May '20

08 May '20

Dear all, I hope you are in staying safe. I'm working on a balanced network consisting of "iaf_cond_exp" neurone model. I want to have the control over these parameters at the same time: - increasing the synaptic decay time. - reducing the synaptic weight. The goal is to make the area under the synaptic current, constant. So that we know that the only parameter that is affecting on the network, is synaptic delay. I wonder if you know of any furmula in mathematics or any algorithm, that can help me, making this process automatic. And keeping the area under the curve, a constant value. Best wishes, Nosratullah

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local_num_threads, multiprocessing and topology.ConnectLayers
by Juan Manuel Vicente 06 May '20

06 May '20

Hi all, I'm trying to understand some inner workings of Nest. Rigth now I'm running simulations with close half millons elements, using mpirun in a cluster with 25 nodes. The problem I am having is that the "setup" (layer creation and connections) phase takes close to 8min and the simulation only takes 1min. So I tried to use python's multiprocessing package to speed it up, with the following code: nest.ResetKernel() nest.SetKernelStatus({"local_num_threads": 1}) #... connections = [ (layer1, layer1, conn_ee_dict, 1), (layer1, layer2, conn_ee_dict, 2), (layer2, layer2, conn_ee_dict, 3), (layer2, layer1, conn_ee_dict, 4) ] # Process the connections. def parallel_topology_connect(parameters): [pre, post, projection, number] = parameters print(f"Connection number: {number}") topology.ConnectLayers(pre, post, projection) pool = multiprocessing.Pool(processes=4) pool.map(parallel_topology_connect, connections) The above example takes around 0.9s, but if the last two to lines are changed for a sequential call, it takes 2.1s: for [pre, post, projection, number] in connections: print(f"Connection number: {number}") topology.ConnectLayers(pre, post, projection) So far the multiprocessing works great, the problem comes when the "local_num_threads" parameters is changed from 1 to 2 or more, in the cluster it could be 32. The code gets stuck in the topology.Connect without any error, after a while I just stopped it. Also I realised that the tolopoly.ConnectLayers just spawn one thread to connects layers despite the local_num_threads is setted more than one. Any idea what is going on? Thanks in advance Juan Manuel PD: The full example code is attached (60 lines of code). The local_num_threads and multiprocessing_flag variables change the behaviors of the code.

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Implementing Synaptic Failure Rate
by Joshua Stern 04 May '20

04 May '20

Hello, I hope everyone is well. I am working on replicating some results from Teremae et al 2012 <https://www.nature.com/articles/srep00485#Sec4> and have run into an area of uncertainty. The paper calls for there to be a synaptic weight-dependent failure rate of synaptic transmission: [image: image.png] Is there any way to implement this in Nest? If it would help, I am working on a custom neuron model built in Nestml. I can also write some C++ if it seems that there would be an easy way to add this functionality with a short function. Alternatively, are there canonical ways to alter network structure to account for such a failure rate if it cannot be implemented in Nest? Thank you and best wishes, Josh Stern

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NEST3 / Some thoughts on NodeCollections and Connect
by Hans Ekkehard Plesser 01 May '20

01 May '20

Dear Colleagues, This mail is mostly relevant for developers, but I would also like interested users to take a look and comment. In https://github.com/nest/nest-simulator/issues/1570#issuecomment-622380622 I have attempted to summarize a number of thoughts about the details of node representation and Connect() semantics. While a lot is in place, we still need to clarify some details in the near future, before we can release NEST 3. Your input is therefore welcome! If you haven't looked at NEST 3 yet, you may want to take a look at https://nest-simulator.readthedocs.io/en/latest/guides/nest2_to_nest3/nest2… first. Best regards, Hans Ekkehard -- Prof. Dr. Hans Ekkehard Plesser Head, Data Science Section 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 Home http://arken.nmbu.no/~plesser

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NEST Conference 2020 goes virtual — Call for Contributions
by Anne Elfgen 30 Apr '20

30 Apr '20

Dear all, The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the NEST Conference 2020. 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. This year's conference will take place as a *virtual conference* on *Monday/Tuesday 29/30 June 2020*. We are now inviting contributions to the conference, including plenary talks, "posters" and breakout sessions on specific topics. *Important dates* *01 June 2020* — Deadline for submission of contributions *08 June 2020* — Notification of acceptance *10 June 2020* — Deadline for NEST Initiative Membership applications (registration is free for members in 2020) *22 June 2020* — Registration deadline *29 June 2020* — NEST Conference 2020 starts For more information on how to submit your contribution, register and participate, please visit the conference website *https://nest-simulator.org/conference* We are looking forward to seeing you all in June! Hans Ekkehard Plesser, Susanne Kunkel, Dennis Terhorst, Anne Elfgen & many more

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error at installing nest2.20 from source code
by Abolfazl Ziaeemehr 29 Apr '20

29 Apr '20

Hello everyone, I used the source code to install nest simulator 2.20 from here <https://nest-simulator.readthedocs.io/en/stable/installation/linux_install.…> and did the following steps (I used to do the same steps for version 2.18 without error.) cmake -Dwith-python=3 -DCMAKE_INSTALL_PREFIX:PATH=</install/path> </path/to/NEST/src> make make install make installcheck I got this error from "make installcheck": Phase 1: Testing if SLI can execute scripts and report errors ------------------------------------------------------------- Running test 'selftests/test_pass.sli'... Running Apr 21 19:46:31 SLIStartup [Error]: NEST_DATA_DIR is not usable: Apr 21 19:46:31 SLIStartup [Error]: Directory '/usr/share/nest' does not exist. Apr 21 19:46:31 SLIStartup [Error]: I'm using the default: /home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/share/nest Apr 21 19:46:31 SLIStartup [Error]: NEST_DOC_DIR is not usable: Apr 21 19:46:31 SLIStartup [Error]: Directory '/usr/share/doc/nest' does not exist. Apr 21 19:46:31 SLIStartup [Error]: I'm using the default: /home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/share/doc/nest /usr/bin//bin/nest /usr/share/doc/nest/selftests/test_pass.sli /home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/bin/nest_serial: 20: /home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/bin/nest_serial: Apr: not found Failed: unexpected exit code 127 Running Apr 21 19:46:31 SLIStartup [Error]: NEST_DATA_DIR is not usable: Apr 21 19:46:31 SLIStartup [Error]: Directory '/usr/share/nest' does not exist. Apr 21 19:46:31 SLIStartup [Error]: I'm using the default: /home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/share/nest Apr 21 19:46:31 SLIStartup [Error]: NEST_DOC_DIR is not usable: Apr 21 19:46:31 SLIStartup [Error]: Directory '/usr/share/doc/nest' does not exist. Apr 21 19:46:31 SLIStartup [Error]: I'm using the default: /home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/share/doc/nest /usr/bin//bin/nest /usr/share/doc/nest/selftests/test_pass.sli /home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/bin/nest_serial: 20: /home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/bin/nest_serial: Apr: not found *** *** An unexpected exit code usually hints at a bug in the test suite! *** *** Please report the problem at *** https://github.com/nest/nest-simulator/issues *** *** To help us diagnose the problem, please attach the archived content *** of these directories to the issue: *** - '/home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/reports' *** - '/home/abolfazl/prog/install-dir/nest-simulator-2.20.0_build/reports/nest.CgbAi' *** CMakeFiles/installcheck.dir/build.make:57: recipe for target 'CMakeFiles/installcheck' failed make[3]: *** [CMakeFiles/installcheck] Error 2 CMakeFiles/Makefile2:99: recipe for target 'CMakeFiles/installcheck.dir/all' failed make[2]: *** [CMakeFiles/installcheck.dir/all] Error 2 CMakeFiles/Makefile2:106: recipe for target 'CMakeFiles/installcheck.dir/rule' failed make[1]: *** [CMakeFiles/installcheck.dir/rule] Error 2 Makefile:188: recipe for target 'installcheck' failed make: *** [installcheck] Error 2 -- regards, Abolfazl Ziaeemehr Department of Physics, PhD student in Computational Physics Institute for Advanced Studies in Basic Sciences (IASBS) Tel: 0919 607 4296 Page: iasbs.ac.ir/~a.ziaeemehr

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Reset while keeping network connections
by Daphne Cornelisse 27 Apr '20

27 Apr '20

Dear Nest community, I have a network of LIF neurons ('iaf_cond_alpha') and I would like to implement STDP synapses, rather than the standard 'static_synapse'. I would like to be able to set a subset of the network to STDP synapses (e.g. all excitatory ones) and leave the rest static. This is how have implemented that currently (see code). I am using a for loop because I want to see how STDP changes the activity over time (over many iterations), which leaves me with the following two questions: *1. Is this a correct way to implement STDP in nest? * *2. Is there a way to reset the network such that the connections are not lost? *ResetNetwork() and ResetKernel() both seem to destroy all the network connections. While I would like to keep them, so that the network becomes less and less randomly connected per simulation. Thank you! Best, Daphne *CODE* # define network connectivity conn_dict = {'rule': 'pairwise_bernoulli', 'p': 0.1} # define dicts for static synapses static_ex_params = {'model':'static_synapse','weight': 6.0, 'delay': 1.5} static_in_params = {'model':'static_synapse','weight': -96.0, 'delay': 1.5} if self.STDP == 'ALL': # all synapses should be stdp ones, make connections between the two populations: # from exc neurons to all neurons nest.Connect(neurons_all[:self.NE], neurons_all, conn_dict, self.syn_params_ex) # from interneurons to all neurons nest.Connect(neurons_all[self.NE:], neurons_all, conn_dict, self.syn_params_in) elif self.STDP == 'EXC': # keep the inhibitory synapses static # connections from exc neurons to all neurons nest.Connect(neurons_all[:self.NE], neurons_all, conn_dict, self.syn_params_ex) # connections from interneurons to all neurons nest.Connect(neurons_all[self.NE:], neurons_all, conn_dict, static_in_params) where synapse_params_in = { 'model':'stdp_synapse', 'lambda': 0.01, 'alpha': 1.0, 'delay': 1.5, 'weight':-95.0 }

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