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?
Dear Nest Community,
Would anyone have insight into why I'm getting an UnknownModelName error?
I'm new to Nest and am using Nest 18.0 on Lubuntu Virtual Box (Windows 10
Thanks for any help.
**** Error Message--additional details attached ******
raise exceptionCls(commandname, message)
nest.ll_api.UnknownModelName: ('UnknownModelName in CopyModel_l_l_D:
/stdp_izh_bitwise_correct_connection is not a known model name. Please
check the modeldict for a list of available models.', 'UnknownModelName',
<SLILiteral: CopyModel_l_l_D>, ': /stdp_izh_bitwise_correct_connection is
not a known model name. Please check the modeldict for a list of available
I created a little tool that provides packaging for NEST extension modules
into python packages that can be `pip installed` on the target machine.
First off, is this interesting to the community? It certainly seems easier
to `pip install some-module` than to provide installation instructions, and
I can now specify my modules as dependencies in code I distribute.
Secondly, there are some shortcomings based on the fact that the pip
install only reliably produces the build artifacts into python's
site-packages and nest doesn't look for them there. This can probably only
be elegantly solved by adding an `entry_point` to the nest python module so
that these pip nest modules can announce themselves there?
Robin De Schepper, MSc
Department of Brain and Behavioral Sciences
Unit of Neurophysiology
University of Pavia, Italy
Via Forlanini 6, 27100 Pavia - Italy
Tel: (+39) 038298-7607
the deadline for submission of contributions is approaching and the NEST
Conference website is down since yesterday.
Is there an alternative way to submit contributions?
Bruno Golosio, PhD
Associate Professor of Applied Physics
Coordinator of the School of Medical Physics
Department of Physics
University of Cagliari (Italy)
Dear Nest Users,
I hope all of you be in health during these times.
I want to create a balanced network as it’s the first step of my master thesis. The model I want to use is “iaf_cond_exp” which is a must for later purposes. There’s an example in Pynest folder of a balanced network, unless it’s using “iaf_psc_alpha” and it doesn’t fit my goals.
When I try to change the model, and run the program, the network doesn’t get active and there’s nothing to record.
I divided my question into two part:
- Does anyone have a balanced network with “iaf_cond_exp” neurons and all of its necessary parameters to run?
- In general, how do people find or calculate their network parameters to fit the neuron model they use and don’t get lost in the massive number of parameters.
Note: I also tried to use the example of “brunel_alpha_evolution_strategies.py” example which is a genetic algorithm to find the best parameter, although it finds the parameters after 50 generation, I use those parameters afterwards. It just doesn’t work!
Just a little reminder that the submission deadline for the (virtual)
NEST Conference 2020 is *this Monday, June 1st. *We are looking forward
to your contributions.
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
This year's conference will take place as a *virtual conference* on
*Monday/Tuesday 29/30 June 2020*.
We are inviting contributions to the conference, including plenary
talks, "posters" and breakout sessions on specific topics.
*01 June 2020* — Deadline for submission of contributions
*08 June 2020* — Notification of acceptance
*10 June 2020* — Deadline for NEST Initiative Membership applications
(registration is free for members in 2020)
*22 June 2020* — Registration deadline
*29 June 2020* — NEST Conference 2020 starts
For more information on how to submit your contribution, register and
participate, please visit the conference website
We are looking forward to seeing you all in June!
Hans Ekkehard Plesser, Susanne Kunkel, Dennis Terhorst, Anne Elfgen &
Dear NEST Users & Developers!
I would like to invite you to our next fortnightly Open NEST Developer
Video Conference on
Monday 25 May, 11.30-12.30 CEST (UTC+2).
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 suggested from the
Review of NEST User Mailing List
Project team round
The agenda for this meeting is also available online, see
Looking forward to seeing you soon!
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.
* 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).
How to log in with a video conferencing system, depends on you VC system
- Using the H.323 protocol (eg Polycom): vc.dfn.net##97938800 or
- Using the SIP protocol:email@example.com
- 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,
I am writing to you regarding two matters:
Reset Network/Kernel in nest2->nest 3
In the last developer conference, Daphne Cornelisse talked about that she used ResetNetwork() to solve her problem.
ResetNetwork() is marked as deprecated. No one said anything, so I got confused why this is apparently the recommended way or at least approved. She showed me, that it works (in her case). It is deprecated, so there is probably some good reasoning behind it. The documentation says: "ResetNetwork is deprecated and will be removed in NEST 3, because this function is not fully able to reset network and simulator state. What are the edge cases where the use causes problems?
In this ticket, it is stated that the feature is just removed with any replacement.
Thus, in nest3 there is only ResetKernel().
This means that you have to rebuild the network for any application where you do multiple simulations with different input or parameter changes. I am using nest3 for reinforcement learning and in each training episode, I have to extract all the weights and save them, reset the kernel, reconstruct the net, then load all the weights. This adds a lot of overhead in performance and bloats my code. I basically have another front-end storing the net and talking to the nest back-end.
Therefore, the update to nest3 is a downgrade for many applications. I don’t have a solution for this issue, but I want to spark some discussion as I learned that I am not the only nest user to stumble into this issue.
STDP Performance boost by manual computation in python
In the paper "Demonstrating Advantages of Neuromorphic Computation: A Pilot Study“ by Wunderlich et al. (https://www.frontiersin.org/articles/10.3389/fnins.2019.00260/full) some performance improvement on STDP was reported.
"The synaptic weight updates in each iteration were restricted to those synapses which transmitted spikes, i.e., the synapses from the active input unit to all output units (32 out of the 1,024 synapses), as the correlation a+ of all other synapses is zero in a perfect simulation without fixed-pattern noise. This has the effect of reducing the overall time required to simulate one iteration[…]“
The provided source code (https://github.com/electronicvisions/model-sw-pong/blob/976e0778ca05cfd96... <https://github.com/electronicvisions/model-sw-pong/blob/976e0778ca05cfd96...>) indeed contains a manual computation of STDP. When using the nest library I don’t expect to do some computation in python to be faster. It appears to me that the nest implementation is computing STDP every time, even without spikes? Maybe someone can comment on this whether this can be improved in nest?
Benedikt S. Vogler
Benedikt S. Vogler
Student M.Sc. Robotics, Cognition, Intelligence
I would like to simulate a network of current based LIF
('iaf_psc_delta') with inhibitory plasticity.
I tried using the model vogels_sprekeler_synapse, but the resulting
weights are positive (and they increase when firing rate is high). I
guess it is because when used with conductance based neurons, the
resulting weights would be multiplied by a gI<0? And with current based
neurons, this is not the case. The weight is directly used as the
synaptic weight. Do I understand it right?
Is there a way to implement inhibitory plasticity using the
vogels_sprekeler_synapse and current based LIF?