- NEST Users - NEST Initiative Mail

by Abbey Durell

I'm trying to set the initial Calcium concentration for this model with no luck. I've tried self.model_params = {'tau_m': 10.0, # membrane time constant (ms) ... 'Ca': 0.1 #initial calcium concentration } This method doesn't throw an exception but it also doesn't change the initial calcium concentration. I've also tried self.nodes_e.set(Ca=0.1) which throws an exception nest.lib.hl_api_exceptions.NESTErrors.DictError: DictError in SLI function SetStatus_id: Unused dictionary items: Ca Any suggestions?

3 days, 2 hours

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Adding gap junctions using NESTML

by bstrohmer＠gmail.com

Hi everyone, has anyone successfully added a gap junction to an existing model using NESTML? I would like to add gap junction support to the aeif_cond_alpha neuron model but it is unclear how this can be done within NESTML. As suggested by Charl, I started to look into updating the jinja templates but I am not sure this will be possible since both the input/output of the neuron and the model's equation must be updated to support gap junctions. If anyone has any suggestions or if anyone has NESTML code for adding a gap junction to an existing model, I would be very appreciative. Thanks!

3 days, 12 hours

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MPI error when using GetConnections

by Xavier Otazu

Hello, I am trying to normalize connection weights using a code similar to the one on the Documentation (see code below). I receive an error when assigning the modified weights on the last line of the for loop below (only when I use several MPI processes). That is, when using mpirun -np 1 python3 test_DumpLayerConnections.py I do not receive any error, but when using 4 MPI processes: mpirun -np 4 python3 test_DumpLayerConnections.py I receive the following error when trying to start a Simulation: Mar 16 11:13:53 NodeManager::prepare_nodes [Info]: Preparing 675 nodes for simulation. [dcccluster:747534] *** An error occurred in MPI_Allgather [dcccluster:747534] *** reported by process [2696478721,0] [dcccluster:747534] *** on communicator MPI_COMM_WORLD [dcccluster:747534] *** MPI_ERR_TRUNCATE: message truncated [dcccluster:747534] *** MPI_ERRORS_ARE_FATAL (processes in this communicator will now abort, [dcccluster:747534] *** and potentially your MPI job) I can avoid this error by commenting the last for loop assignament (the normalized weights assignment operation). I am using NEST 3.3 (compiled with MPI option) and Python 3.8.12. I guess it could be related to the DumpLayerConnections bug I told you some days ago. Thanks a lot in advance! Xavier import nest import numpy as np pos = nest.spatial.grid(shape = [30,30] ) input = nest.Create('iaf_psc_alpha', positions=pos) layer_0 = nest.Create('iaf_psc_alpha', positions=pos) layer_1 = nest.Create('iaf_psc_alpha', positions=pos) conn_neur = {'rule':'pairwise_bernoulli', 'use_on_source': True, 'mask': {'grid':{'shape':[9,9]}} } nest.CopyModel('static_synapse', 'syn_1_model') syn_0 = {'synapse_model': 'static_synapse'} syn_1 = {'synapse_model': 'syn_1_model'} nest.Connect(input, layer_0, conn_neur, syn_0) nest.Connect(input, layer_1, conn_neur, syn_1) nest.DumpLayerConnections(input, layer_0, 'static_synapse', 'conn.txt') nest.DumpLayerConnections(input, layer_1, 'syn_1_model', 'conn.txt') for neuron in layer_0: conn = nest.GetConnections(target=neuron, synapse_model='static_synapse') w = np.array(conn.weight) if (w.size>1): w_normed = w / sum(abs(w)) # L1-norm w_nparray = 2. * w_normed conn.weight = w_nparray.tolist() nest.Simulate(100)

6 days, 17 hours

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Open NEST Developer Video Conference Monday Mar 13 2023 11:30 CET (UTC+1)

by Dennis Terhorst

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference, today Monday March 13, 11.30-12.30 CET (UTC+1). 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. 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. 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/2023-03-13-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

1 week, 3 days

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Coredump with DumpLayerConnections

by Xavier Otazu

Hello, For the following code, I receive a coredump (it throws and assertion from grid_layer.h) (output at the end). When not connecting 'input' to 'layer_1', everything works fine. It looks like it does not like to connect a layer to more than one target layer. Thanks a lot in advance! import nest pos = nest.spatial.grid(shape = [3,3] ) input = nest.Create('iaf_psc_alpha', positions=pos) layer_0 = nest.Create('iaf_psc_alpha', positions=pos) layer_1 = nest.Create('iaf_psc_alpha', positions=pos) conn_neur = {'rule':'pairwise_bernoulli', 'use_on_source': True, 'mask': {'grid':{'shape':[3,3]}} } nest.Connect(input, layer_0, conn_neur) nest.Connect(input, layer_1, conn_neur) nest.DumpLayerConnections(input, layer_0, 'static_synapse', 'conn.txt') The output message is: python3: /home/neurobit/src/nest_3.3_src/nestkernel/grid_layer.h:223: nest::Position<D> nest::GridLayer<D>::lid_to_position(nest::index) const [with int D = 2; nest::index = long unsigned int]: Assertion `lid < dims_[ 0 ]' failed. [dcccluster:326996] *** Process received signal *** [dcccluster:326996] Signal: Aborted (6) [dcccluster:326996] Signal code: (-6) [dcccluster:326996] [ 0] /lib64/libpthread.so.0(+0x12c20)[0x7fb317ec0c20] [dcccluster:326996] [ 1] /lib64/libc.so.6(gsignal+0x10f)[0x7fb31739637f] [dcccluster:326996] [ 2] /lib64/libc.so.6(abort+0x127)[0x7fb317380db5] [dcccluster:326996] [ 3] /lib64/libc.so.6(+0x21c89)[0x7fb317380c89] [dcccluster:326996] [ 4] /lib64/libc.so.6(+0x2fa76)[0x7fb31738ea76] [dcccluster:326996] [ 5] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(+0x1d7783)[0x7fb2fcbf7783] [dcccluster:326996] [ 6] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(_ZNK4nest9GridLayerILi2EE12get_positionEm+0xa)[0x7fb2fcbf779a] [dcccluster:326996] [ 7] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(_ZN4nest5LayerILi2EE16dump_connectionsERSoSt10shared_ptrINS_14NodeCollectionEES3_INS_13AbstractLayerEERK5Token+0x525)[0x7fb2fcbfca75] [dcccluster:326996] [ 8] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(_ZN4nest22dump_layer_connectionsERK5TokenSt10shared_ptrINS_14NodeCollectionEES5_R12lockPTRDatumISoXadL_ZN14SLIInterpreter11OstreamtypeEEEE+0x35c)[0x7fb2fcc1530c] [dcccluster:326996] [ 9] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(_ZNK4nest10NestModule37DumpLayerConnections_os_g_g_lFunction7executeEP14SLIInterpreter+0xfc)[0x7fb2fcb491dc] [dcccluster:326996] [10] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libsli.so(_ZN13FunctionDatum7executeEP14SLIInterpreter+0x2b)[0x7fb2f7c0975b] [dcccluster:326996] [11] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libsli.so(_ZN14SLIInterpreter8execute_Em+0x1b2)[0x7fb2f7c06b32] [dcccluster:326996] [12] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libsli.so(_ZN14SLIInterpreter7executeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x1ea)[0x7fb2f7c06e7a] [dcccluster:326996] [13] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/pynestkernel.so(+0x38669)[0x7fb2ff200669] [dcccluster:326996] [14] /lib64/libpython3.8.so.1.0(+0x173db8)[0x7fb31846adb8] [dcccluster:326996] [15] /lib64/libpython3.8.so.1.0(_PyEval_EvalFrameDefault+0x976)[0x7fb3184d8406] [dcccluster:326996] [16] /lib64/libpython3.8.so.1.0(_PyFunction_Vectorcall+0x1bf)[0x7fb3184967af] [dcccluster:326996] [17] /lib64/libpython3.8.so.1.0(_PyEval_EvalFrameDefault+0x5da)[0x7fb3184d806a] [dcccluster:326996] [18] /lib64/libpython3.8.so.1.0(_PyEval_EvalCodeWithName+0x3e7)[0x7fb3184953c7] [dcccluster:326996] [19] /lib64/libpython3.8.so.1.0(_PyFunction_Vectorcall+0x422)[0x7fb318496a12] [dcccluster:326996] [20] /lib64/libpython3.8.so.1.0(_PyEval_EvalFrameDefault+0x5da)[0x7fb3184d806a] [dcccluster:326996] [21] /lib64/libpython3.8.so.1.0(_PyFunction_Vectorcall+0x1bf)[0x7fb3184967af] [dcccluster:326996] [22] /lib64/libpython3.8.so.1.0(_PyEval_EvalFrameDefault+0x54d9)[0x7fb3184dcf69] [dcccluster:326996] [23] /lib64/libpython3.8.so.1.0(_PyEval_EvalCodeWithName+0x3e7)[0x7fb3184953c7] [dcccluster:326996] [24] /lib64/libpython3.8.so.1.0(PyEval_EvalCode+0x23)[0x7fb3184965a3] [dcccluster:326996] [25] /lib64/libpython3.8.so.1.0(+0x22b09a)[0x7fb31852209a] [dcccluster:326996] [26] /lib64/libpython3.8.so.1.0(+0x250fa2)[0x7fb318547fa2] [dcccluster:326996] [27] /lib64/libpython3.8.so.1.0(+0x10312c)[0x7fb3183fa12c] [dcccluster:326996] [28] /lib64/libpython3.8.so.1.0(PyRun_SimpleFileExFlags+0x369)[0x7fb318400fe3] [dcccluster:326996] [29] /lib64/libpython3.8.so.1.0(Py_RunMain+0x2ef)[0x7fb318549b6f] [dcccluster:326996] *** End of error message ***

1 week, 3 days

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Re: how to customize Ion channels and receptor types of "cm_default" neuron model through nestml

by 王梓瑞

Hi, Charl Thanks for your reply. And sorry about taking such a long time in this question. My main research interest is the biophysical model of glutamatergic cortico-striatal projections. I previous constructed a striatal circuit (contain two neuron populations MSN and FSI) using simple Integrate-and-fire model, and it successfully reproduced the firing state of striatum MSN and FSI. In that case I simplified the glutamatergic cortical input from multi-synapse to single synapse. Since glutamatergic cortico-striatal projections consist of AMPA and NMDA receptors, I then changed the simple Integrate-and-fire model to multi-synapse Integrate-and-fire model using NESTML. But the results didn't seem good, and got even worse when I handly tuned some parameters. I have been confused for a long time and I thought my multi-synapse definition might account for this. I read about "cm_default" neuron model on NEST model directory, which you described containing AMPA_NMDA receptors. And the difference between your definition on this receptor and mine is the term of NMDA ratio (ratio of NMDA versus AMPA channels). So I thought that might help me resolve this issue and that's why I wrote to ask how to customize receptor types of "cm_default" neuron model through NESTML. So briefly: I wonder how I can apply the NMDA ratio in my own multi-synapse Integrate-and-fire model or is it possible to extract the AMPA_NMDA receptors from "cm_default". And I read about the "cm_default" neuron model on NEST model directory, and you described like "For receptors, the choice is AMPA, GABA or NMDA or AMPA_NMDA. Ion channels and receptor types can be customized with NESTML." ------------------------------------------------------------------ Sender:Charl Linssen <nest-users(a)turingbirds.com> Sent At:2022 Dec. 8 (Thu.) 16:56 Recipient:users <users(a)nest-simulator.org> Subject:[NEST Users] Re: how to customize Ion channels and receptor types of "cm_default" neuron model through nestml Dear Zirui, Thank you for writing in. Just to double-check, the compartmental ("cm_default") model in NEST is intended for use in morphologically detailed models, containing dozens or hundreds of compartments. If your model can be described by only a few compartments, it could be easier to manually define these (define say a separate membrane potential and dendritic potential and the coupling between them; see https://nestml.readthedocs.io/en/v5.1.0/tutorials/active_dendrite/nestml_ac… <https://nestml.readthedocs.io/en/v5.1.0/tutorials/active_dendrite/nestml_ac… > for an example). If you do indeed need the full morpological complexity in your simulations, then the cm_default model is indeed the way to go. We are currently working on extending NESTML to support defining the biophysics in NESTML (ion channels, membrane dynamics etc.) and then combining this with a morphology in NEST. For a prototype of this functionality, please see the branch in https://github.com/nest/nestml/pull/772 <https://github.com/nest/nestml/pull/772 >. The current effort there is focused on adding the ability to specify different ion channels in a more flexible manner. If you could describe a little bit more about your use case and what exactly you are trying to achieve, we could probably give you some more specific advice. Cheers, Charl On Mon, Dec 5, 2022, at 05:54, 王梓瑞 wrote: Dear nest community, I wonder if any of you know how to customize Ion channels and receptor types of "cm_default" neuron model through nestml. I noticed some information on Extending NESTML- Running NESTML with custom templates, but I still found myself confused due to my poor understanding. Is there any way to acquire the .nestml file of the cm_default neuron model? Thank you. Best, Zirui _______________________________________________ NEST Users mailing list -- users(a)nest-simulator.org <mailto:users@nest-simulator.org > To unsubscribe send an email to users-leave(a)nest-simulator.org <mailto:users-leave@nest-simulator.org >

2 weeks

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Ubuntu issue with version 3.4 and roll back to version 3.3

by Ing Jyh Tsang

Hi, I am using Ubuntu and just updated to Nest 3.4 however I get the following error: Feb 26 15:53:59 SLIStartup [Fatal]: SLI initialisation file not found at /build/nest-xF9H0B/nest-3.4/debian/nest/usr/share/nest/sli/sli-init.sli. Please check your NEST installation. Moreover, is there a simple way to roll back and install Nest 3.3 , such as: sudo apt install nest=3.3? Thanks, Inton

2 weeks, 2 days

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Deepcopy problem with connections

by Xavier Otazu

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__

3 weeks, 1 day

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Free NESTML and NEST Desktop Workshop (27.03.2023)

by Jens Bruchertseifer

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/

3 weeks, 3 days

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NEST Conference 2023 – Save the date & registration

by Hans Ekkehard Plesser

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

3 weeks, 3 days

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