Dear NEST developer,
I would like to initialize a network with neurons and connections and then
run many simulations with it. It would be great if I could build the
network one time and then deepcopy (or something similar) for each
simulation. Is something like this possible?
Thanks,
Andrew Lehr
Dear nest developer,
Is there a pre-implemented nest model
allowing multiplicative aggregation of compartments/dendritic branches.
I am looking for a model that will allow
multiplicative aggregation of two dendritic branches at the soma.
I very much appreciate any support provided.
Best regards,
Benedikt
Hello,
I am fairly new to NEST and I have been struggling with the following issue.
Github repo: https://github.com/angpapadi/wm-net
My simulation code consists of 3 python files:
- sim.py, the main simulation script, where the model is built and the simulation program is specified
- helpers.py a collection of helper functions
- parameters.py a script where all parameters are specified.
In order to be able to run the code successfully, I have to set certain parameters to unrealistic values. If I try to move any of these parameters towards a more realistic regime, NEST crashes (see end of post for full error message). Most relevant is the fact that setting these parameters to more reasonable values, increases by a lot the amount of spikes that NEST has to keep track of.
Examples of such parameters that currently have wrong values:
> the DOWNSCALE_FACTOR, that essentially controls the neural network's size. Right now my network size is downscaled 95% of what I would want it to be. There are 2 hypercolumns with 4 minicolumns each. Each minicolumn consists of 4 neurons.
> E_rev is a list of the reversal potential for each synaptic port. My neuron model has 3 synaptic ports for AMPA, NMDA and GABA, in that order. The Erev for GABA is correctly set but for AMPA and NMDA I cannot get it to work for values bigger than -25, when the correct reversal potential for both is 0.
I cannot find the issue with my code, if there is any. When the simulation is very limited (the aforementioned parameters are set to unrealistic values), the code runs flawlessly and produces the expected plots and spike rasters. Also, if I allocate more resources by running on multiple mpi processes, I can be slightly closer to the correct parameter ranges before the nest::badproperty error occurs.
To reproduce the error:
1. You can optionally first run the code as is to verify that it works (by running the sim.py script)
2. Then try setting one of the parameters mentioned above to a more plausible value. You can do so by going to the parameters.py file and either reducing the DOWNSCALE_FACTOR parameter (line 7) or increasing the first two entries of the E_rev list (line 203) that correspond to the AMPA and NMDA reversal potentials, or doing both if you 're feeling adventurous!
The error is the following:
terminate called after throwing an instance of 'nest::BadProperty'
what(): BadProperty
[59eb791543aa:00113] *** Process received signal ***
[59eb791543aa:00113] Signal: Aborted (6)
[59eb791543aa:00113] Signal code: (-6)
[59eb791543aa:00113] [ 0] /lib/x86_64-linux-gnu/libc.so.6(+0x3ef20)[0x7f2c117b7f20]
[59eb791543aa:00113] [ 1] /lib/x86_64-linux-gnu/libc.so.6(gsignal+0xc7)[0x7f2c117b7e97]
[59eb791543aa:00113] [ 2] /lib/x86_64-linux-gnu/libc.so.6(abort+0x141)[0x7f2c117b9801]
[59eb791543aa:00113] [ 3] /usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0x8c957)[0x7f2c0a55a957]
[59eb791543aa:00113] [ 4] /usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0x92ab6)[0x7f2c0a560ab6]
[59eb791543aa:00113] [ 5] /usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0x91b19)[0x7f2c0a55fb19]
[59eb791543aa:00113] [ 6] /usr/lib/x86_64-linux-gnu/libstdc++.so.6(__gxx_personality_v0+0x2a8)[0x7f2c0a560488]
[59eb791543aa:00113] [ 7] /lib/x86_64-linux-gnu/libgcc_s.so.1(+0x10613)[0x7f2c0d6f1613]
[59eb791543aa:00113] [ 8] /lib/x86_64-linux-gnu/libgcc_s.so.1(_Unwind_Resume+0x125)[0x7f2c0d6f1e95]
[59eb791543aa:00113] [ 9] /opt/nest/lib/python3.6/site-packages/nest/../../../libnestkernel.so(_ZN4nest20EventDeliveryManager18gather_spike_data_INS_9SpikeDataEEEviRSt6vectorIT_SaIS4_EES7_+0x10ed)[0x7f2c0af0684d]
[59eb791543aa:00113] [10] /opt/nest/lib/python3.6/site-packages/nest/../../../libnestkernel.so(+0xf4894)[0x7f2c0aee4894]
[59eb791543aa:00113] [11] /usr/lib/x86_64-linux-gnu/libgomp.so.1(GOMP_parallel+0x3f)[0x7f2c08d79ecf]
[59eb791543aa:00113] [12] /opt/nest/lib/python3.6/site-packages/nest/../../../libnestkernel.so(_ZN4nest17SimulationManager7update_Ev+0x151)[0x7f2c0aee0f61]
[59eb791543aa:00113] [13] /opt/nest/lib/python3.6/site-packages/nest/../../../libnestkernel.so(_ZN4nest17SimulationManager12call_update_Ev+0x5a5)[0x7f2c0aee1915]
[59eb791543aa:00113] [14] /opt/nest/lib/python3.6/site-packages/nest/../../../libnestkernel.so(_ZN4nest17SimulationManager3runERKNS_4TimeE+0x1d3)[0x7f2c0aee6713]
[59eb791543aa:00113] [15] /opt/nest/lib/python3.6/site-packages/nest/../../../libnestkernel.so(_ZN4nest17SimulationManager8simulateERKNS_4TimeE+0x1c)[0x7f2c0aee699c]
[59eb791543aa:00113] [16] /opt/nest/lib/python3.6/site-packages/nest/../../../libnestkernel.so(_ZN4nest8simulateERKd+0xc2)[0x7f2c0aecb972]
[59eb791543aa:00113] [17] /opt/nest/lib/python3.6/site-packages/nest/../../../libnestkernel.so(_ZNK4nest10NestModule16SimulateFunction7executeEP14SLIInterpreter+0x43)[0x7f2c0ae97a53]
[59eb791543aa:00113] [18] /opt/nest/lib/python3.6/site-packages/nest/../../../libsli.so(_ZN13FunctionDatum7executeEP14SLIInterpreter+0x43)[0x7f2c0a8e9063]
[59eb791543aa:00113] [19] /opt/nest/lib/python3.6/site-packages/nest/../../../libsli.so(_ZN14SLIInterpreter8execute_Em+0x222)[0x7f2c0a8e6d42]
[59eb791543aa:00113] [20] /opt/nest/lib/python3.6/site-packages/nest/../../../libsli.so(_ZN14SLIInterpreter7executeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x15e)[0x7f2c0a8e72be]
[59eb791543aa:00113] [21] /opt/nest/lib/python3.6/site-packages/nest/pynestkernel.so(+0x29ac8)[0x7f2c0c5caac8]
[59eb791543aa:00113] [22] python[0x50a94c]
[59eb791543aa:00113] [23] python(_PyEval_EvalFrameDefault+0x449)[0x50c5b9]
[59eb791543aa:00113] [24] python[0x509d48]
[59eb791543aa:00113] [25] python[0x50aa7d]
[59eb791543aa:00113] [26] python(_PyEval_EvalFrameDefault+0x449)[0x50c5b9]
[59eb791543aa:00113] [27] python[0x508245]
[59eb791543aa:00113] [28] python[0x589471]
[59eb791543aa:00113] [29] python(PyObject_Call+0x3e)[0x5a067e]
[59eb791543aa:00113] *** End of error message ***
Let me know if you need any additional information and I thank you for your time
Angeliki
Hi NEST-Team,
To use the NEST-Simluator in an interactive installation, I would need to
1) have access to the spike in the network in realtime, and
2) send signals (spikes) to neurons in the simulation in realtime.
For 1), I could imagine writing the spike-times to a file and then reading that file while the simulation is running. However, I currently don’t see an option for 2). Then I heard that you are actually working on module that would enable realtime I/O-access. Is that the case? And, if so, is there a timeline?
Best,
Benjamin
-- -- --
Dr. Benjamin Staude | Paul-Lincke-Ufer 7 | 10999 Berlin | benjamin.staude(a)gmail.com
Hello,
I have an issue regarding random generation in nest version 2.18 with
Python3.6.
I attach a small python script that replicates my issue (less than 50
lines).
In short, I am trying to create two Poisson generators and connect them to
the same neuron.
I noticed that if I reverse the order by which these Poisson generators are
created (using the nest.Create()) function, then I get a different membrane
potential response over time.
In the code, noise_exc is created before noise_inh. If that order is
reversed, the plot is different.
My hunch is that this has to do with how nest generates random numbers.
So I tried to force the creation of Poisson generators by providing a seed
(lines 15 and 18).
However this behavior persists.
Do you have any insights as to why this happens?
At the end of the script (lines 32-43) I provide a trivial case of the
desired behavior I am trying to replicate.
Thank you for your time
Angeliki
--
[image: Kth Logo]
Angeliki Papadimitriou
Research EngineerKTH Royal Institute of Technology
*School of Electrical Engineering and Computer Science (EECS)**Division of
Computational Science and Technology (CST)*
Computational Brain Science Unit
Room 4438, Floor 4, Lindstedtsvägen 5
11428 Stockholm, Sweden
angpap(a)kth.se <namn(a)kth.se>
Dear NEST Users & Developers!
I would like to invite you to this years first Open NEST Developer Video Conference, tomorrow
Monday 20 January, 11.30-12.30 CET (UTC+1).
As usual the agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2020-01-20-Open-NEST-Developer-…
For details how to log in see below.
Agenda
* Welcome
* Preparing NEST 2.20 release
* Preparing merge of nest-3 branch to master
* Review of NEST User Mailing List
* Review of open Github Pull Request
* Review of open Github Issues
Looking forward to seeing you soon!
Hans Ekkehard Plesser
------------------
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Dear all,
I tried to install a custom module into Nest first using the MyModule
example as described here
https://nest.github.io/nest-simulator/extension_modules
The installation works fine without errors, however when I try to load
the model into pynest with
import nest
import('mymodule')
I get a "Segmentation fault (core dumped)". The last traceback of
debbugging with python pdb is the following:
-> engine.run('{%s} runprotected' % decode(cmd))
(Pdb) s
--Call--
>
/home/user/.local/lib/python2.7/site-packages/nest/__init__.py(107)decode()
-> def decode(s):
(Pdb) s
>
/home/user/.local/lib/python2.7/site-packages/nest/__init__.py(108)decode()
-> return s.decode('utf-8')
(Pdb) s
--Call--
> /usr/lib/python2.7/encodings/utf_8.py(15)decode()
-> def decode(input, errors='strict'):
(Pdb) s
> /usr/lib/python2.7/encodings/utf_8.py(16)decode()
-> return codecs.utf_8_decode(input, errors, True)
(Pdb) s
--Return--
> /usr/lib/python2.7/encodings/utf_8.py(16)decode()->(u'(cereb...
Install', 26)
-> return codecs.utf_8_decode(input, errors, True)
(Pdb) s
--Return--
>
/home/user/.local/lib/python2.7/site-packages/nest/__init__.py(108)decode()->u'(mymod...
Install'
-> return s.decode('utf-8')
(Pdb) s
Segmentation fault (core dumped)
I already added the model path to my LD_LIBRARY_PATH variable so that
the module can be found, but now it cannot be loaded.
It looks like a decoding issue, but I don't know what went wrong during
the installation. Do you have any idea what could be the issue? I am
using python 2.7 and NEST 2.3.1
Thanks!
Benedikt Feldotto
--
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
Hello everyone,
Happy New Year! I want to create a simple neural network consisting of a single neuron connected by multiple inputs (spike generators). I am aware that I can create single spike generators and then connect them but it becomes infeasible as the size of input grows. Is there a way to create a network of maybe 100 spike generators connected to a single LIF Neuron without explicitly creating 100 of them? Once the connections are made is there a way that the network is updated when the weights are updated. I want the network to get updated as I update the weights. Now, I update the weights and reset the network to create it again with new weights. Any help would be appreciated. Thanks in advance!
Hi all,
I have a question about nest user creation/usage in nest docker entrypoint file: I am working with Sarus and Singularity and due to some permission limits I can't create a new user inside a container. I have tried to modify the entrypoint.sh file as follows
https://github.com/ChristopherBignamini/nest-docker/blob/no_nest_user_creat…
in order to skip the creation of the nest user. I am not a nest expert but everything seems to work, at least if I try to run a couple of examples like one_neuron.py and twoneurons.py.
My question is: what is the reason behind the creation and usage of the nest user?
Thank you in advance.
Cheers
Christopher