Dear Colleagues,
The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NESTSimulator to the NEST Conference 2024.
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 explicitly encourage young scientists to participate in the conference!
This year's conference will again take place as a virtual conference on Monday/Tuesday 17/18 June 2024.
We are delighted to welcome
* Tadashi Yamazaki (University of Electro-Communications, Tokyo)
* Behnam Ghazinouri (Ruhr University Bochum)
* Benedetta Gambosi (Politecnico di Milano)
* Tobias Gemmeke (RWTH Aachen University)
as keynote speakers at the conference.
Registration and submission of contributions are now open!
We are inviting contributions to the conference, including talks, "posters" and workshops on specific topics.
Please register and submit your contribution(s) via the conference website
https://nest-simulator.org/conference.
Important dates
15 April 2024 - Deadline for submission of contributions
26 April 2024 - Deadline for NEST Initiative membership applications eligible for fee reduction
08 May 2024 - Notification of acceptance
07 June 2024 - Registration deadline
17 June 2024 - NEST Conference 2024 starts
We are looking forward to seeing you all in June!
Hans Ekkehard Plesser and the conference organizing committee
[Image]
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Prof. Dr. Hans Ekkehard Plesser
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
Dear NEST Users & Developers!
I would like to invite you to our next fortnightly Open NEST Developer Video Conference today
*Monday February 26, 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
o MPI tests port to Python #3102 <https://github.com/nest/nest-simulator/pull/3102>
The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2024-02-26-Open-NEST-Developer-… <https://github.com/nest/nest-simulator/wiki/2024-02-26-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
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Forschungszentrum Juelich GmbH
52425 Juelich
Sitz der Gesellschaft: Juelich
Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498
Vorsitzender des Aufsichtsrats: MinDir Stefan Müller
Geschaeftsfuehrung: Prof. Dr. Astrid Lambrecht (Vorsitzende),
Karsten Beneke (stellv. Vorsitzender), Dr. Ir. Pieter Jansens
----------------------------------------------------------------------
Dear all,
I’m trying to run nestml with the latest nest master branch commit (8e85268ca512e0f509240fb5ef06f62a4e61a67e) but I get an error when calling the `nest.Install()` function.
-- Install configuration: ""
-- Installing: /Users/wybo/miniconda3/envs/nest-multiscale/lib/nest/concmech_mockup_module.so
Traceback (most recent call last):
File "/Users/wybo/Code/NEST/nestml/tests/nest_compartmental_tests/compile_test.py", line 23, in <module>
nest.Install("concmech_mockup_module")
File "/Users/wybo/miniconda3/envs/nest-multiscale/lib/python3.12/site-packages/nest/ll_api.py", line 216, in stack_checker_func
return f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File "/Users/wybo/miniconda3/envs/nest-multiscale/lib/python3.12/site-packages/nest/lib/hl_api_simulation.py", line 332, in Install
return sr("(%s) Install" % module_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/wybo/miniconda3/envs/nest-multiscale/lib/python3.12/site-packages/nest/ll_api.py", line 103, in catching_sli_run
raise exceptionCls(commandname, message)
nest.lib.hl_api_exceptions.NESTErrors.DictError: DictError in SLI function SLIInterpreter::execute: Key '/Install' does not exist in dictionary.
Did anybody experience the same problem, or know what might cause it?
Best regards,
Willem
----------------------
Dr. Willem Wybo
Peter Grünberg Institute 15 - Neuromorphic Software Ecosystems
Forschungszentrum Jülich
www.fz-juelich.de/pgi/PGI-15
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------------------------------------------------------------------------------------------------
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), Dr. Ir. Pieter Jansens
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Dear NEST Community,
while adapting my model to run on multiple MPI processes, I have been running into some problems connected with the usage of masks inside the connectivity dictionary for a 2D-spatially distributed population.
You can find a minimal example containing further details in the attachments in the form of a .txt file. Please just change "txt" with "py" for execution. The following explanation also references to the example.
My setup:
- NEST version 3.4
- Python version 3.10
- Executed with mpirun inside a conda environment
"mpirun -np 4 python3 minEx_4MPIprocesses_problem.py"
- System: Ubuntu 22.04
- The same problem occurred when the model was executed on JURECA.
Problem description:
While using multiple MPI processes:
1. Create circular mask. See line 119
Create a spatial 2D-Population of neurons "neurons". See line 125-132
2. Select some of the neurons as source neurons. See line 154
3. Set up the connection dictionary with a mask inside. See line 157-163
4. For each source neuron: See line 173
Connect the source neurons with the neurons population using the connection dictionary
See line 183
Where does the Problem occur?:
The error occurs at the point when the nest.Connect(…) call is executed in the loop at line 183 when each source neuron is connected.
When does the Problem occur?:
Its occurrance depends on whether or not a mask is used inside the connection-dict given to the nest.Connect(...) function.
If the mask is removed as in the conn-dict. in line 164-169, no error is produced in none of the used settings (however the result is not as desired).
If the mask is used, the way the problem shows itself depends on the number of MPI processes used, and on the setting of the number of neurons, extent, mask radius, number of source neurons and if edge wrap is used.
- For 1 and 2 MPI procs the model is running correctly, independent of the used conn-dict and setting.
- For 3 MPI procs either
the model runs but the distance between connected neurons does not correspond to the given mask dimensions. In other words: The established connections are longer/shorter than the mask should allow.
Or execution leads to an error code output ("segmentation fault") from mpirun and job abortion.
The terminal output for an example run can be seen in the attached file "minEx_4MPIprocesses_problem_error_output_3_MPI"
- For 4 MPI procs either,
the model runs correctly,
or it leads to an error code output ("segmentation fault") and the model not terminating. (When executed in the terminal the keyboard command "str+c" is needed to stop the execution. The terminal output for an example run can be seen in the attached file "minEx_4MPIprocesses_problem_error_output_4_MPI"
If the model is executed with the same setting on 3 and 4 MPI procs there are 3 possible combinations of above described problems.
1. The model runs and terminates on both, however the distances created on 3 MPI procs are wrong.
2. The model works with 4 MPI procs but creates the "segmentation fault" error on 3 MPI processes.
3. The model creates a "segmentation fault" error on both.
Please keep in mind that whether an error occurs and in which form highly depends on the used setting. In the minimal example I provide different settings which represent the above described cases. However, it might not cover everything that can occur.
Workaround:
For my own use-case I found a rather computing-time consuming workaround.
It involves applying nest.SelectNodesByMask() on every source neuron, and from the resulting set choosing the targets using the desired probability.
This requires multiple additional loops and also communicating the position data of every neuron to every MPI process in the beginning.
Using this approach, the connection distances seem to be correct and no error occurs while executing the model. However, while writing this I'm questioning if I actually tested it enough. So there might be some not yet discovered problems.
Is there something that I overlooked or approached wrong when using masks in the connectivity dict on multiple MPI processes?
Thanks in advance!
Best,
Miriam Kempter
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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), Dr. Ir. Pieter Jansens
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Hi!
I am trying to implement weight normalization (as you show in your documentation). It works well (see below code) when executed "normally" (or when using" mpirun -np 1"), but it hangs (it just do not respond) when executed using mpirun. It hangs when I try to execute nest.Simulate() after weigth assignment. If I remove the "conn.weight = w_nparray" instruction in the following code, it works without hang.
I use NEST 3.6 and python3, compiled with python, mpi, sionlib and music.
Thanks a lot in advance!
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_0)
nest.DumpLayerConnections(input, layer_0, 'static_synapse', 'conn.txt')
nest.DumpLayerConnections(input, layer_1, 'syn_1_model', 'conn.txt')
nest.Simulate(100)
for neuron in layer_0:
conn = nest.GetConnections(source=input, 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
nest.Simulate(100)
I’m trying to run NEST on Jureca, but I get an error:
Traceback (most recent call last):
File "/p/project/jinm60/users/wybo1/Code/NEAST_models/BBP/tests/run_all_tests.py", line 2, in <module>
import nest.lib.hl_api_exceptions as nestexceptions
File "/p/home/jusers/wybo1/jureca/nest/nest-install/lib64/python3.11/site-packages/nest/__init__.py", line 64, in <module>
from .ll_api_kernel_attributes import KernelAttribute # noqa
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/p/home/jusers/wybo1/jureca/nest/nest-install/lib64/python3.11/site-packages/nest/ll_api_kernel_attributes.py", line 22, in <module>
from .ll_api import spp, sps, sr, stack_checker
File "/p/home/jusers/wybo1/jureca/nest/nest-install/lib64/python3.11/site-packages/nest/ll_api.py", line 53, in <module>
from . import pynestkernel as kernel # noqa pylint: disable=no-name-in-module
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ImportError: libltdl.so.7: cannot open shared object file: No such file or directory
Apparently it doesn’t find pynestkernel.so (I think?). However, this file is definitely there.
wybo1@jrlogin09 ~/.../site-packages/nest> pwd
/p/home/jusers/wybo1/jureca/nest/nest-install/lib64/python3.11/site-packages/nest
wybo1@jrlogin09 ~/.../site-packages/nest> ls
__init__.py __pycache__ lib ll_api.py ll_api_kernel_attributes.py logic math pynest-init.sli pynestkernel.so random raster_plot.py server spatial spatial_distributions versionchecker.py visualization.py voltage_trace.py
The problem also only manifests itself while running from a SLURM script.
I followed the install instruction here:
https://nest-simulator.readthedocs.io/en/stable/installation/noenv_install.…
And source the nest_vars.sh file according to the instructions:
`source /p/home/jusers/wybo1/jureca/nest/nest-install/bin/nest_vars.sh`
Also, `make installcheck` passes successfully (both from the login node and from a slurm script).
Cheers,
Willem
----------------------
Dr. Willem Wybo
Peter Grünberg Institute 15 - Neuromorphic Software Ecosystems
Forschungszentrum Jülich
www.fz-juelich.de/pgi/PGI-15<http://www.fz-juelich.de/pgi/PGI-15>
----------------------
------------------------------------------------------------------------------------------------
------------------------------------------------------------------------------------------------
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), Dr. Ir. Pieter Jansens
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Dear all,
I am trying to develop a new synapse model that uses the activity of the postsynaptic neuron. I'm looking at the STDP synapse to understand how it gets the target neuron history, and I wanted to clarify: are t_spike and t_lastspike the time stamps for the last spike of the presynaptic or postsynaptic neuron? I am looking at the send method of stdp_synapse.h (https://github.com/nest/nest-simulator/blob/91a02ed98017224deabe25e8e08bcfd…)
Thanks in advance!
Best,
Priyanka Rao
Hi,
We've discovered the following problem in restoring network simulation: if the simulation stops for some reason restoring it form the network status kept in files does not yield the same results as in case when the simulation was not interrupted and finishes normally.
In our experiment we run a script twice as follows:
1. Create a network and simulate for some period of time.
2. Record the current network status in terms of connections and membrane potentials and store them in files at the end of script.3. End the script
4. Start the script again restoring the network structure from files.5. Run simulation till the end of desired time.
The spiking activity of the network is different if we run simulation without interruption or if we run it in the manner described here: https://nest-simulator.readthedocs.io/en/stable/auto_examples/store_restore…
i.e. if we run a script with following steps:1. Run simulation for some time.2. Get connections and membrane potentials3. Reset kernel4. Create again the network5. Simulate till the end of desired time.
Please help to resolve the problem.
Best,Petia Koprinkova-Hristova
Hi,
We recently discovered that multimeter returns membrane potential in some unordered way so if we want to record the last state of a group of neurons and then to continue simulation from exactly the same state we were unable to reconstruct it properly.
Is there a way to record the membrane potential together with neuron ID?
Best,Petia Koprinkova-Hristova