Hello NEST Users,
I wanted to create a neural network with generalized LIF Neurons. I do not want any exponential or alpha shaped PSCs. Since I cannot draw to describe its shape, I am posting a link from google images to help understand what I mean by a generalized LIF.
https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.researchgate.net%2Ffi…
Thanks in advance!
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
Hi,
I wanted to know how to stop printing the simulation details on my terminal window. For instance I can see the number of open mpi process, simulation time being printed every time I run a model. I do not want to see the details on my terminal screen, is there a way to do it?
Hi NEST-team,
I would like to run a simulation of > 1000 neurons, and to make my
computing time faster I am looking into how to use parallel computing with
NEST.
I have seen this <https://www.nest-simulator.org/parallel-computing/> page
but I can't seem to find anything about using the cloud (e.g. google cloud,
colab etc.) and the use of GPU's. I am quite new to this so it could be I
missed something.
- Is it possible to use a GPU for the nest simulations?
- Are the some resources I could learn from?
Thank you!
Best,
Daphne
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!
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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-…
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Agenda
* Welcome
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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