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Building a nest simulation for mnist dataset
by Aditya Srivastava 10 Feb '25

10 Feb '25

I have built a nest simulation for a binary dataset where we have two classes normal and anomalous . So i tried to built a model iun such a way so that the model learns to produce no spike in case of a normal data but when it sees a anomalous data it is producing the spikes. So this model was working fine. Now I am trying to move forward with it and try it on image dataset where I am using simple MNIST data and I have just taken image for one digit and trying to build a same kind of architecture where it should produce almost no spike when it sees a digit one and oin case of other digits it should produce spikes but I am facing issues in it . The model is not able to produce enough spikes and when I am creating more exc connections then spikes are not reducing with each epochs so can you please suggest me a better approach or some suggestion for my current approach. # loading dataset from sklearn.datasets import fetch_openml import numpy as np def preprocess_mnist_digit(digit=1): """ Load and preprocess the MNIST dataset to only include images of a specific digit. Normalize pixel values by dividing by 4 and flatten images. Args: digit (int): The digit to filter (e.g., 1 for digit '1'). num_samples (int): Number of samples to retain. Returns: flattened_images (array): Flattened MNIST images of the specified digit. labels (array): Corresponding labels. """ # Load MNIST data mnist = fetch_openml('mnist_784', version=1) data, labels = mnist['data'], mnist['target'].astype(int) # Convert labels to integers # Filter images that correspond to the specified digit digit_indices = np.where(labels == digit)[0] # Get indices where label is equal to the specified digit # Extract the corresponding images and labels filtered_images = data.iloc[digit_indices].to_numpy(dtype=float) / 4.0 # Normalize filtered_labels = labels[digit_indices] return filtered_images, filtered_labels # Load only images of the digit '1' images, labels = preprocess_mnist_digit(digit=1) # building simulation nest.ResetKernel() # Simulation parameters dt = 0.01 # Simulation time step in ms nest.SetKernelStatus({"resolution": dt}) N = 784 # Number of neurons images=images[:10] gmax = 3 neuron_params = { "tau_m": 140.0, # Membrane time constant in ms "E_L": -70.6, # Resting potential (in mV) "V_th": -40.4, # Threshold potential (in mV) "V_reset": -70.6, # Reset potential (in mV) "V_m":0.0, } nest.CopyModel("stdp_synapse", "stdp_synapse_exc", {"weight":gmax,"alpha":1,"Wmax":gmax, "tau_plus": 20.0,"lambda": -0.0005, "mu_minus": 0, "mu_plus": 0}) nest.CopyModel("stdp_synapse", "stdp_synapse_inh", {"weight":-gmax/(N-1),"alpha":1,"Wmax":-gmax, "tau_plus": 20.0,"lambda": 0.005, "mu_minus": 0, "mu_plus": 0}) G_spike_gen = nest.Create("poisson_generator",N) G_input = nest.Create("parrot_neuron", N) input_spike_recorder = nest.Create("spike_recorder",N) nest.Connect(G_input, input_spike_recorder,"one_to_one") G_hidden = nest.Create("iaf_psc_delta", N,params=neuron_params) G_output = nest.Create("iaf_psc_delta", 1,params=neuron_params) hidden_spike_recorder = nest.Create("spike_recorder",N) nest.Connect(G_hidden, hidden_spike_recorder,"one_to_one") S_in = nest.Connect(G_spike_gen, G_input, "one_to_one") # Connect input to hidden layer with excitatory STDP synapses S_exc = nest.Connect(G_input, G_hidden,"one_to_one",syn_spec = "stdp_synapse_exc") # Retrieve the neuron IDs for G_input and G_hidden input_ids = G_input.tolist() hidden_ids = G_hidden.tolist() # Get the existing excitatory connections and extract their source-target ID pairs exc_connections = nest.GetConnections(G_input, G_hidden) exc_connection_pairs = set(zip(exc_connections.get("source"), exc_connections.get("target"))) for i in input_ids: for h in hidden_ids: if (i, h) not in exc_connection_pairs: nc1 = nest.NodeCollection([i]) nc2 = nest.NodeCollection([h]) nest.Connect(nc1, nc2, syn_spec= "stdp_synapse_inh") S_out = nest.Connect(G_hidden, G_output,"all_to_all",syn_spec = {"weight": 40.4/N}) # training function def train_net(images, spike_tol=0): output_spike_recorder = nest.Create("spike_recorder") output_membrane_recorder = nest.Create("voltmeter", 1) nest.Connect(G_output, output_spike_recorder) # Record output layer spikes nest.Connect(output_membrane_recorder, G_output, "one_to_one") while True: for image in images: for i in range(784): G_spike_gen[i].rate=image[i] nest.Simulate(500) for i in range(784): spikes = nest.GetStatus(hidden_spike_recorder[i], "events")[0] # if length of spikes is not zero then print the number of spikes and the times of spikes also print the index of the input_spike_recorder if len(spikes["times"]) != 0: print("Number of spikes in hidden_spike_recorder",i,":", len(spikes["times"])) print("Times of spikes in hidden_spike_recorder",i,":", spikes["times"]) print("\n") output_spikes_num = len(output_spike_recorder.get()["events"]["times"]) print("# of output spikes: " + str(output_spikes_num)) # del recorder_for_epoch if output_spikes_num <= spike_tol: print("Training finished") break return output_membrane_recorder, output_spike_recorder v, spk = train_net(images, spike_tol = 2) ___________________________________________

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Open NEST Developer Video Conference Monday February 10, at 11:30 CET (UTC+0100)
by Dennis Terhorst 10 Feb '25

10 Feb '25

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference today *Monday February 10, at 11:30 CET (UTC+0100).* As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing and planned work in the team and highlight 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/2025-02-10-Open-NEST-Developer-… <https://github.com/nest/nest-simulator/wiki/2025-02-10-Open-NEST-Developer-…> Looking forward to seeing you soon! Cheers, Dennis Terhorst Log-in information We use a virtual conference room provided by DFN <https://www.dfn.de/en/> (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 <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 <mailto: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 <http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4> -- Dipl.-Phys. Dennis Terhorst Coordinator Software Development Institute for Advanced Simulation (IAS-6) Computational and Systems Neuroscience & JARA-Institute Brain Structure-Function Relationships (INM-10) Institute of Neuroscience and Medicine Jülich Research Center, Member of the Helmholz Association 52425 Jülich, Germany http://www.csn.fz-juelich.de/ Phone +49 2461 61-85062 ---------------------------------------------------------------------- Forschungszentrum Jülich GmbH 52425 Jülich Sitz der Gesellschaft: Jülich Eingetragen im Handelsregister des Amtsgerichts Düren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Stefan Müller Geschäftsführung: Prof. Dr. Astrid Lambrecht (Vorsitzende), Karsten Beneke (stellv. Vorsitzender), Prof. Dr. Ir. Pieter Jansens ----------------------------------------------------------------------

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Invitation to EBRAINS events in Heidelberg from 12-13 March
by Dennis Terhorst 10 Feb '25

10 Feb '25

Hi everyone, I would like to forward the invitation to the EBRAINS events in Heidelberg next month. Many experts of tools will be there including quite a number of NEST users and developers. You have the opportunity to join tutorials, learn about more interesting tools and directly exchange experience with others. Have a look! --- Dear NEST Community, We are delighted to invite you to join us in Heidelberg, Germanyat theEINC <https://einc.eu/>, from 12-13 March 2025, for a series of EBRAINS events. On*Wednesday,**12 March*, we will host an*EBRAINS Tutorials and Users Day*. It is aimed at both newcomers and experienced users of EBRAINS tools. Newcomers will have the chance to explore the wide range of Open Science Tools and Services available on the research infrastructure, while seasoned EBRAINS users can dive deeper with advanced tutorials and discuss with developers. You can find out more about the Tutorials and Users Day here:https://www.ebrains.eu/news-and-events/accelerate-your-neuroscience-research-with-ebrains-tutorials-and-users-day-in-heidelberg <https://www.ebrains.eu/news-and-events/accelerate-your-neuroscience-researc…> On*Thursday, 13 March*, we will host an*EBRAINS Developers Day*. It will feature introductions for developers on how to integrate tools and services into the EBRAINS software distribution or to integrate services via EBRAINS (workflows). You can find out more about the Developers Day here:https://www.ebrains.eu/news-and-events/developers-day-2025 <https://www.ebrains.eu/news-and-events/developers-day-2025> If you have any questions, please don't hesitate to contactcommunications(a)ebrains.eu <mailto:communications@ebrains.eu>. We hope to see you in Heidelberg! Kind regards, EBRAINSAISBL --- -- Dipl.-Phys. Dennis Terhorst Coordinator Software Development Institute for Advanced Simulation (IAS-6) Computational and Systems Neuroscience & JARA-Institute Brain Structure-Function Relationships (INM-10) Institute of Neuroscience and Medicine Jülich Research Center, Member of the Helmholz Association 52425 Jülich, Germany http://www.csn.fz-juelich.de/ Phone +49 2461 61-85062 ---------------------------------------------------------------------- Forschungszentrum Jülich GmbH 52425 Jülich Sitz der Gesellschaft: Jülich Eingetragen im Handelsregister des Amtsgerichts Düren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Stefan Müller Geschäftsführung: Prof. Dr. Astrid Lambrecht (Vorsitzende), Karsten Beneke (stellv. Vorsitzender), Prof. Dr. Ir. Pieter Jansens ----------------------------------------------------------------------

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Test
by Jochen Martin Eppler 10 Feb '25

10 Feb '25

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Building a nest simulation for mnist dataset
by Aditya Srivastava 28 Jan '25

28 Jan '25

I have built a nest simulation for a binary dataset where we have two classes normal and anomalous . So i tried to built a model iun such a way so that the model learns to produce no spike in case of a normal data but when it sees a anomalous data it is producing the spikes. So this model was working fine. Now I am trying to move forward with it and try it on image dataset where I am using simple MNIST data and I have just taken image for one digit and trying to build a same kind of architecture where it should produce almost no spike when it sees a digit one and oin case of other digits it should produce spikes but I am facing issues in it . The model is not able to produce enough spikes and when I am creating more exc connections then spikes are not reducing with each epochs so can you please suggest me a better approach or some suggestion for my current approach. # loading dataset from sklearn.datasets import fetch_openml import numpy as np def preprocess_mnist_digit(digit=1): """ Load and preprocess the MNIST dataset to only include images of a specific digit. Normalize pixel values by dividing by 4 and flatten images. Args: digit (int): The digit to filter (e.g., 1 for digit '1'). num_samples (int): Number of samples to retain. Returns: flattened_images (array): Flattened MNIST images of the specified digit. labels (array): Corresponding labels. """ # Load MNIST data mnist = fetch_openml('mnist_784', version=1) data, labels = mnist['data'], mnist['target'].astype(int) # Convert labels to integers # Filter images that correspond to the specified digit digit_indices = np.where(labels == digit)[0] # Get indices where label is equal to the specified digit # Extract the corresponding images and labels filtered_images = data.iloc[digit_indices].to_numpy(dtype=float) / 4.0 # Normalize filtered_labels = labels[digit_indices] return filtered_images, filtered_labels # Load only images of the digit '1' images, labels = preprocess_mnist_digit(digit=1) # building simulation nest.ResetKernel() # Simulation parameters dt = 0.01 # Simulation time step in ms nest.SetKernelStatus({"resolution": dt}) N = 784 # Number of neurons images=images[:10] gmax = 3 neuron_params = { "tau_m": 140.0, # Membrane time constant in ms "E_L": -70.6, # Resting potential (in mV) "V_th": -40.4, # Threshold potential (in mV) "V_reset": -70.6, # Reset potential (in mV) "V_m":0.0, } nest.CopyModel("stdp_synapse", "stdp_synapse_exc", {"weight":gmax,"alpha":1,"Wmax":gmax, "tau_plus": 20.0,"lambda": -0.0005, "mu_minus": 0, "mu_plus": 0}) nest.CopyModel("stdp_synapse", "stdp_synapse_inh", {"weight":-gmax/(N-1),"alpha":1,"Wmax":-gmax, "tau_plus": 20.0,"lambda": 0.005, "mu_minus": 0, "mu_plus": 0}) G_spike_gen = nest.Create("poisson_generator",N) G_input = nest.Create("parrot_neuron", N) input_spike_recorder = nest.Create("spike_recorder",N) nest.Connect(G_input, input_spike_recorder,"one_to_one") G_hidden = nest.Create("iaf_psc_delta", N,params=neuron_params) G_output = nest.Create("iaf_psc_delta", 1,params=neuron_params) hidden_spike_recorder = nest.Create("spike_recorder",N) nest.Connect(G_hidden, hidden_spike_recorder,"one_to_one") S_in = nest.Connect(G_spike_gen, G_input, "one_to_one") # Connect input to hidden layer with excitatory STDP synapses S_exc = nest.Connect(G_input, G_hidden,"one_to_one",syn_spec = "stdp_synapse_exc") # Retrieve the neuron IDs for G_input and G_hidden input_ids = G_input.tolist() hidden_ids = G_hidden.tolist() # Get the existing excitatory connections and extract their source-target ID pairs exc_connections = nest.GetConnections(G_input, G_hidden) exc_connection_pairs = set(zip(exc_connections.get("source"), exc_connections.get("target"))) for i in input_ids: for h in hidden_ids: if (i, h) not in exc_connection_pairs: nc1 = nest.NodeCollection([i]) nc2 = nest.NodeCollection([h]) nest.Connect(nc1, nc2, syn_spec= "stdp_synapse_inh") S_out = nest.Connect(G_hidden, G_output,"all_to_all",syn_spec = {"weight": 40.4/N}) # training function def train_net(images, spike_tol=0): output_spike_recorder = nest.Create("spike_recorder") output_membrane_recorder = nest.Create("voltmeter", 1) nest.Connect(G_output, output_spike_recorder) # Record output layer spikes nest.Connect(output_membrane_recorder, G_output, "one_to_one") while True: for image in images: for i in range(784): G_spike_gen[i].rate=image[i] nest.Simulate(500) for i in range(784): spikes = nest.GetStatus(hidden_spike_recorder[i], "events")[0] # if length of spikes is not zero then print the number of spikes and the times of spikes also print the index of the input_spike_recorder if len(spikes["times"]) != 0: print("Number of spikes in hidden_spike_recorder",i,":", len(spikes["times"])) print("Times of spikes in hidden_spike_recorder",i,":", spikes["times"]) print("\n") output_spikes_num = len(output_spike_recorder.get()["events"]["times"]) print("# of output spikes: " + str(output_spikes_num)) # del recorder_for_epoch if output_spikes_num <= spike_tol: print("Training finished") break return output_membrane_recorder, output_spike_recorder v, spk = train_net(images, spike_tol = 2)

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Open NEST Developer Video Conference Monday January 27, at 11:30 CET (UTC+0100)
by Dennis Terhorst 27 Jan '25

27 Jan '25

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference today *Monday January 27, at 11:30 CET (UTC+0100).* As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing and planned work in the team and highlight 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 o Welcome NEAT * 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/2025-01-27-Open-NEST-Developer-… <https://github.com/nest/nest-simulator/wiki/2025-01-27-Open-NEST-Developer-…> Looking forward to seeing you soon! Cheers, Dennis Terhorst Log-in information We use a virtual conference room provided by DFN <https://www.dfn.de/en/> (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 <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 <mailto: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 <http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4> -- Dipl.-Phys. Dennis Terhorst Coordinator Software Development Institute for Advanced Simulation (IAS-6) Computational and Systems Neuroscience & JARA-Institute Brain Structure-Function Relationships (INM-10) Institute of Neuroscience and Medicine Jülich Research Center, Member of the Helmholz Association 52425 Jülich, Germany http://www.csn.fz-juelich.de/ Phone +49 2461 61-85062 ---------------------------------------------------------------------- Forschungszentrum Jülich GmbH 52425 Jülich Sitz der Gesellschaft: Jülich Eingetragen im Handelsregister des Amtsgerichts Düren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Stefan Müller Geschäftsführung: Prof. Dr. Astrid Lambrecht (Vorsitzende), Karsten Beneke (stellv. Vorsitzender), Prof. Dr. Ir. Pieter Jansens ----------------------------------------------------------------------

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Error during NESTML make
by Beck Strohmer 16 Jan '25

16 Jan '25

Hi, I am trying to create a dopamine modulated synapse using NESTML. You can find the nestml file and the file for creating the neuron attached. I am using NEST version 3.8.0 and running within a conda environment (v. 24.1.2). I get a make error in the end so the files are created except for the .so file that I need for loading the model. I attached the terminal error for further details. Has anyone else had this issue? How were you able to resolve it? Thanks for any advice! Best, Beck

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Save the date: NEST Conference 2025 will take place 17–18 June (online)
by Hans Ekkehard Plesser 16 Jan '25

16 Jan '25

Dear Colleagues, The NEST Initiative is excited to invite everyone interested in neural simulation technology, simulation-based neuroscience and NEST Software to the NEST Conference 2025. Save the date for the virtual conference, taking place online on 17–18 June 2025! The annual 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! Registration and abstract submission will open soon. For more information on the event please visit the conference website https://nest-simulator.org/conference We are looking forward to seeing you in June! Hans Ekkehard Plesser on behalf of the conference organizing committee -- Prof. Dr. Hans Ekkehard Plesser Research Committee Chair, Faculty of Science and Technology Head, Department of Data Science 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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Using nest server to run nestml models
by Pablo Alejandro 16 Jan '25

16 Jan '25

Hi Everyone, Apologies for sending the message below again. I think my last post was done incorrectly and no one got notifications. ----------------------------------------- Does nest server currently support nestml models? I couldn't find examples in the documentation (even though `nestml-server` binary exists in nest3.8 docker image). Ideally, I would like to run it with MPI too. I can run a simple example (`python test.py`) with `nest-server` running from a docker container, but the same code fails if I use `nestml-server`: ``` docker run -it --rm -e LOCAL_USER_ID=`id -u $USER` -p 52425:52425 nest/nest-simulator:3.8 nest-server start ``` works, but ``` docker run -it --rm -e LOCAL_USER_ID=`id -u $USER` -p 52426:52426 nest/nest-simulator:3.8 nestml-server start ``` fails with ``` Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/urllib3/connection.py", line 199, in _new_conn sock = connection.create_connection( File "/usr/local/lib/python3.10/dist-packages/urllib3/util/connection.py", line 85, in create_connection raise err File "/usr/local/lib/python3.10/dist-packages/urllib3/util/connection.py", line 73, in create_connection sock.connect(sa) ConnectionRefusedError: [Errno 111] Connection refused The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py", line 789, in urlopen response = self._make_request( File "/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py", line 495, in _make_request conn.request( File "/usr/local/lib/python3.10/dist-packages/urllib3/connection.py", line 441, in request self.endheaders() File "/usr/lib/python3.10/http/client.py", line 1278, in endheaders self._send_output(message_body, encode_chunked=encode_chunked) File "/usr/lib/python3.10/http/client.py", line 1038, in _send_output self.send(msg) File "/usr/lib/python3.10/http/client.py", line 976, in send self.connect() File "/usr/local/lib/python3.10/dist-packages/urllib3/connection.py", line 279, in connect self.sock = self._new_conn() File "/usr/local/lib/python3.10/dist-packages/urllib3/connection.py", line 214, in _new_conn raise NewConnectionError( urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPConnection object at 0x7fb8b63ea440>: Failed to establish a new connection: [Errno 111] Connection refused The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/lib/python3/dist-packages/requests/adapters.py", line 439, in send resp = conn.urlopen( File "/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py", line 843, in urlopen retries = retries.increment( File "/usr/local/lib/python3.10/dist-packages/urllib3/util/retry.py", line 519, in increment raise MaxRetryError(_pool, url, reason) from reason # type: ignore[arg-type] urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=52425): Max retries exceeded with url: /exec (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fb8b63ea440>: Failed to establish a new connection: [Errno 111] Connection refused')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/pablo/git/nest-client/test_server.py", line 4, in <module> response = nsc.from_file('examples/NESTClient_script.py', return_vars='n_events') File "/home/pablo/git/nest-client/nest_client/nest_client.py", line 68, in from_file return self.exec_script(script, return_vars) File "/home/pablo/git/nest-client/nest_client/nest_client.py", line 56, in exec_script response = requests.post(self.url + 'exec', json=params, headers=self.headers) File "/usr/lib/python3/dist-packages/requests/api.py", line 119, in post return request('post', url, data=data, json=json, **kwargs) File "/usr/lib/python3/dist-packages/requests/api.py", line 61, in request return session.request(method=method, url=url, **kwargs) File "/usr/lib/python3/dist-packages/requests/sessions.py", line 544, in request resp = self.send(prep, **send_kwargs) File "/usr/lib/python3/dist-packages/requests/sessions.py", line 657, in send r = adapter.send(request, **kwargs) File "/usr/lib/python3/dist-packages/requests/adapters.py", line 516, in send raise ConnectionError(e, request=request) requests.exceptions.ConnectionError: HTTPConnectionPool(host='localhost', port=52425): Max retries exceeded with url: /exec (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7fb8b63ea440>: Failed to establish a new connection: [Errno 111] Connection refused')) ``` The simple python code used is shown below. ``` from nest_client import NESTClient nsc = NESTClient() response = nsc.from_file('examples/NESTClient_script.py', return_vars='n_events') n_events = response['data'] print('Number of events:', n_events) ``` No nestml models are being tested for now because I don't know which functions should be used to generate code with `nest-client` e.g. ``` from pynestml.codegeneration.nest_code_generator_utils import NESTCodeGeneratorUtils module_name, neuron_model_name, synapse_model_name = \ NESTCodeGeneratorUtils.generate_code_for( "neuron.nestml", "synapse.nestml", post_ports=["post_spikes"], codegen_opts=codegen_opts ) ``` should be rewritten to use `from nest_client import NESTClient`. Thanks in advance, Pablo Alejandro

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Segmentation faults with Structural Plasticity in NEST v3.6 onwards
by Ady Sharma 15 Jan '25

15 Jan '25

Hello, TL;DR: using structural plasticity with mpi-based simulation leads to spontaneous crashes in NESTv3.6 onward. A minimal script reproducing the crash is provided This is a followup to my previous mailing list post (https://www.nest-simulator.org/mailinglist/hyperkitty/list/users@nest-simul…) where I encountered segmentation faults while executing structural-plasticity-based simulations in NESTv3.8. Previously, I suspected that the crashes occurred due to presumed buggy installations of NEST on HPCs. The helpful comments on that post did not solve the issue, so I performed some tests and gathered feedback from other users testing structural plasticity (SP) in NESTv3.6+. My conclusion is that MPI-based simulations spontaneously crash when using structural plasticity. The probability of spontaneous crashes increases with the number of MPI processes. Below, I'll provide more details and also a link to a minimal code that reproduces the segmentation fault. Background: I use SP to perform large-scale network simulations on HPC. Hitherto, I've been using NESTv2.20.1 for various reasons. When I found that SP functionality in NESTv3.6 was finally equivalent to that of NESTv2.20.1, I decided to port my code to the latest release (v3.8). I installed v3.8 on my local machine with MPI support, and tested a scaled-down experiment as a sanity check---everything worked as expected. However, when I ran the full-scale network on HPCs (JUSUF at Jülich and NEMO at uni-freiburg), I got segmentation faults. HPC support couldn't help in resolving the issues. Reinstalling didn't help. At this point, I reached out with the aforementioned post on the mailing list ("MPI-based error on v3.8"). Current Status: I created a minimal pynest script that simulates an E-I network, where the E-->E connections are organised by structural plasticity. I believe this minimal example includes the necessary steps involved in any SP-based experiment. The script can be run by the following BASH call: `mpirun python minimal.py $RANDOM_SEED $TOTAL_NUM_VIRTUAL_PROCS # use srun, when required` The following table gives a summary of the segmentation-fault crashes for `minimal.py`: NEST version machine (MPI procs count) Crash v2.20.1 local (2) No v2.20.2 local (2) No v2.20.1 HPC-2 (upto 1024) No v2.20.2 HPC-1 (upto 1024) No v3.8 local (2) Yes (rarely) v3.8 HPC-1 (8) No v3.8 HPC-1 (16, 24, 32, 64, 128) Yes v3.8 HPC-2 (8) No v3.8 HPC-2 (16, 24, 32, 64, 128) Yes v3.6 HPC-1 (32, 64, 128, 256) Yes - machines: - local: Linux 5.4.0-204-generic x86_64; Intel Core i5-5300U; 16GB - HPC-2: NEMO (https://www.nemo.uni-freiburg.de/) - HPC-1: JUSUF (https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/jusuf) - NESTv2.20.1, v2.20.2, and v3.8 in all cases were installed manually. v3.6 was available as a preinstalled module in HPC-1 - The crashes do not occur if MPI is not used for SP simulations - The crashes do not occur in MPI-based simulations that do not involve SP Conclusion: - MPI-based crashes occur in SP simulations. - Perhaps how MPI-procs handle data in NESTv3.6+ leads to spontaneous segmentation faults. - MPI-proc count potentially correlates with segmention-fault frequency. It is possible that faults will occur, although rarely, with small no. of MPI procs Expectations from this post: Large no. of MPI-procs provide a very significant speed-up for SP-based simulations, therefore it is important that MPI is fully functional in NEST. - Kindly let me know if you can reproduce the same crash. If what I observe is indeed true, then this merits creating an issue on GitHub. - If you cannot reproduce the crashes, I would greatly appreciate any help and fixing the issue. Code: - You will find a formated version of this post with acocmpanying code and crash dumps on this GitLab page: https://gitlab.rz.uni-freiburg.de/as2013/minimal_share.git - minimal.py: https://gitlab.rz.uni-freiburg.de/as2013/minimal_share/-/blob/119e52d3552a7… Thanks! Best, Ady PS. Is it possible to add some sort of text formatting (HTML/markdown) to these posts?

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