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NEST Conference 2020 — Submission deadline 1 June 2020
by Anne Elfgen 26 May '20

26 May '20

Dear all, Just a little reminder that the submission deadline for the (virtual) NEST Conference 2020 is *this Monday, June 1st. *We are looking forward to your contributions. 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 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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Open NEST Developer Video Conference Monday 25 May 2020 11:30 CEST (UTC+2)
by Dennis Terhorst 25 May '20

25 May '20

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference on Monday 25 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/2020-05-25-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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Reset Kernel + STDP Performance
by Benedikt S. Vogler 22 May '20

22 May '20

Hello everyone! I am writing to you regarding two matters: Reset Network/Kernel in nest2->nest 3 In the last developer conference, Daphne Cornelisse talked about that she used ResetNetwork() to solve her problem. ResetNetwork() is marked as deprecated. No one said anything, so I got confused why this is apparently the recommended way or at least approved. She showed me, that it works (in her case). It is deprecated, so there is probably some good reasoning behind it. The documentation says: "ResetNetwork is deprecated and will be removed in NEST 3, because this function is not fully able to reset network and simulator state. What are the edge cases where the use causes problems? In this ticket, it is stated that the feature is just removed with any replacement. https://github.com/nest/nest-simulator/issues/525 Thus, in nest3 there is only ResetKernel(). This means that you have to rebuild the network for any application where you do multiple simulations with different input or parameter changes. I am using nest3 for reinforcement learning and in each training episode, I have to extract all the weights and save them, reset the kernel, reconstruct the net, then load all the weights. This adds a lot of overhead in performance and bloats my code. I basically have another front-end storing the net and talking to the nest back-end. Therefore, the update to nest3 is a downgrade for many applications. I don’t have a solution for this issue, but I want to spark some discussion as I learned that I am not the only nest user to stumble into this issue. STDP Performance boost by manual computation in python In the paper "Demonstrating Advantages of Neuromorphic Computation: A Pilot Study“ by Wunderlich et al. (https://www.frontiersin.org/articles/10.3389/fnins.2019.00260/full) some performance improvement on STDP was reported. "The synaptic weight updates in each iteration were restricted to those synapses which transmitted spikes, i.e., the synapses from the active input unit to all output units (32 out of the 1,024 synapses), as the correlation a+ of all other synapses is zero in a perfect simulation without fixed-pattern noise. This has the effect of reducing the overall time required to simulate one iteration[…]“ The provided source code (https://github.com/electronicvisions/model-sw-pong/blob/976e0778ca05cfd96c4… <https://github.com/electronicvisions/model-sw-pong/blob/976e0778ca05cfd96c4…>) indeed contains a manual computation of STDP. When using the nest library I don’t expect to do some computation in python to be faster. It appears to me that the nest implementation is computing STDP every time, even without spikes? Maybe someone can comment on this whether this can be improved in nest? Kind regards, Benedikt S. Vogler -- Benedikt S. Vogler benedikt.s.vogler(a)tum.de Student M.Sc. Robotics, Cognition, Intelligence

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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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