NEST Users March 2026

users@nest-simulator.org

4 participants
5 discussions

Re: Heaviside function in a Kernel?
by Charl Linssen 18 Mar '26

18 Mar '26

Dear Jeanne, I have taken a look at the code that Zhang et al. made available on GitHub, but I'm afraid that to really grok it I have to do a deep dive. I am not sure why the authors would take the max-value of the postsynaptic response rather than accumulating them; could it be related to a conductance based mechanism like shunting inhibition? Your grid search fit in any case looks like a promising approximation. In conversation with Prof. Markus Diesmann, he pointed me to another way in which the polynomial system could be integrated. Please see the example in this paper (Rotter et al. 1999) <https://brainworks.biologie.uni-freiburg.de/1999/journal%20papers/RotterDie…> on the bottom left of page 385. Using this method, you should be able to integrate a numerically exact (up to floating point precision) solution of the original polynomial. I made a little test script that performs this simulation and also clips the polynomial to zero (see the bottom panel in the plot below). Hopefully this will be useful to you as a basis for further work. Please don't hesitate to get/stay in touch about further steps in the implementation. With kind regards, Charl On Thu, Mar 5, 2026, at 07:04, Jeanne Barthelemy wrote: > > Dear Charl, > > Thank you very much for your follow up. > I agree that using a linear system of ODEs would be preferable. I made a proxy version that uses a beta kernel (inspired from iaf_cond_beta_neuron.nestml). The rise and decay time constants, and a modulatory parameter to scale the conductance, may be tuned to approximate the polynomials derived by Zhang and colleagues. In the attachment, my result is provided from a simple grid search, comparing the proxy with two exponentials and the polynomial from Zhang and colleagues. > > It may be interesting indeed to perform a ReScience paper comparing the beta kernel version in NESTML with the version from Zhang and colleagues (for your information, their matlab code is available on github). However, in order to do so, another part of their implementation puzzles me. Their total model takes into account the polynomial component, but also multiplied by an inhibition efficiency that relies on the firing rate of the ChI population. In their code, from my understanding, they compute the inhibition at time i + k via a multiplication (efficiency due to ChI firing at time i X inhibition along time via the value of the polynomial at time k), but keep only the maximum value along i and k, rather than actually summing the effect of spikes at distinct times. I am not sure to understand the reason between this modelling choice. Do you think it may be interesting as well to compare this with a more traditional approach in NESTML, where the efficiency is computed online at the time t of the simulation, and the beta kernel allows to integrate all past spikes? Even if we integrate a new modulatory parameter for scaling the entire inhibition response and fit better the average behavior of the model from Zhang and colleagues． > > Please do not hesitate to let me know your thoughts about this. > Thank you, > Kind regards, > Jeanne Barthelemy > > > *From:* Charl Linssen <nest-users(a)turingbirds.com> > *Sent:* Thursday, March 5, 2026 1:47 AM > *To:* users(a)nest-simulator.org <users(a)nest-simulator.org> > *Subject:* [NEST Users] Re: Heaviside function in a Kernel? > > Dear Jeanne, > > Much obliged for the link to the paper and clarification. If a postsynaptic response is well described by a polynomial, it makes total sense to use that as part of model fitting. From a simulation perspective, it may be more efficient though to describe the response in the form of a (preferably linear) system of ODEs. Therefore if you could use the alpha or beta functions for the postsynaptic response, that would indeed be an ideal solution. > > That being said, please let me know if this could create an interesting opportunity for a ReScience paper comparing the responses of the alpha versus the polynomial; we could then look into doing a precise simulation of the polynomial more deeply. > > My colleagues also kindly attended me to the possibility of using the so-called Linear Chain Trick in the context of delay differential equations, which could be an attractive alternative method for fitting and simulating postsynaptic responses. > > Cheers, with kind regards, > Charl > > > On Tue, Mar 3, 2026, at 06:04, Jeanne Barthelemy wrote: >> Dear Charl, >> >> Thank you very much for this relevant answer. >> I was trying to reproduce with NESTML the model from this paper: Zhang, Yan-Feng, et al. "An axonal brake on striatal dopamine output by cholinergic interneurons." *Nature Neuroscience* 28.4 (2025): 783-794.". Though, I am starting to think that approximating their model with an alpha kernel, without any need for an heaviside function, may be a better option in my case. >> >> Thank you very much, >> Kind regards, >> Jeanne Barthelemy >> >> >> *From:* Charl Linssen <nest-users(a)turingbirds.com> >> *Sent:* Monday, March 2, 2026 11:47 PM >> *To:* users(a)nest-simulator.org <users(a)nest-simulator.org> >> *Subject:* [NEST Users] Re: Heaviside function in a Kernel? >> >> Dear Jeanne, >> >> Thank you for writing in and thank you for this interesting question. I'm sorry that the advanced kernel shape you need is not supported by the numerical backend. The central issue with this kernel is that it cannot be described by a linear and time-invariant system. As such it makes adding contributions from different spikes much harder, because you cannot simply add the effects of different spikes together (something that only makes sense mathematically for LTI systems). >> >> Actually, there are two components that make this a tricky kernel to work with: the factor *t*^2 as well as the Heaviside function. May I ask how you obtained the shape and parameters of this kernel? Would it be possible to replace it with an LTI kernel like the “alpha” or “beta” function? (You can have a look at the neuron models in NESTML for a definition of both.) >> >> As for the Heaviside function: I tried to think of an approach to implement this behaviour. One thing you could do is to create an additional buffer in the model that corresponds to the 200 ms time interval. When a spike comes in (let's say at time *t*_sp), it would then not only trigger the kernel response (ignoring the Heaviside term), but also add an entry to the buffer, so that 200 ms later, a *negative *contribution would be added to the state, cancelling out the value at that time to reset the value back to zero. The magnitude of this negative contribution would be of equal magnitude to the kernel at time *t_*sp + 200 ms, but with an opposite sign, so that it cancels out. I believe this would work well, even with multiple spikes arriving all the time. >> >> If you can confirm that you are interested in making an implementation of this, I would be happy to look at it together. >> >> Much obliged, >> Charl >> >> >> On Fri, Feb 27, 2026, at 10:20, Jeanne Barthelemy wrote: >>> Dear all, >>> >>> This is a question about best practices for NESTML. I would like to build in the equations block a Kernel, such as for instance: >>> >>> "kernel inh_time_end = ((-0.00002*t*t)/(pow(tau,2.0)) + 0.0087*t/tau + 0.006)/tau" >>> >>> , but modulated by the heaviside function H depending on 200 - t (i.e., H(200.0 ms -t)). To sum up, I would like to get the following kernel: >>> >>> "kernel inh_time_end_modulated = H(200.0 ms -t)*((-0.00002*t*t)/(pow(tau,2.0)) + 0.0087*t/tau + 0.006)/tau" >>> >>> There does not appear to be a direct implementation of the heaviside function H in nestml. I tried to build my own function to convert booleans to 1.0 or 0.0 in my NestML model, upon the condition "200.0 ms > t", and use that instead of the heaviside, but the model is really slow to build on my laptop. I tried other versions with if conditions in the update block, or ternary operators, but they all led to bugs. So, I am unsure of the correct practice to implement an heaviside-modulated polynomial kernel. If anyone has a good and fast solution to propose I would be very happy to learn about it ! >>> >>> Thank you very much, >>> Kind regards, >>> Jeanne BARTHELEMY >>> _______________________________________________ >>> NEST Users mailing list -- users(a)nest-simulator.org >>> To unsubscribe send an email to users-leave(a)nest-simulator.org >>> >> >> >> *CAUTION:* This email originated from outside the organization. Do not click links or open attachments unless you recognize the sender and know the content is safe. >> >> >> _______________________________________________ >> NEST Users mailing list -- users(a)nest-simulator.org >> To unsubscribe send an email to users-leave(a)nest-simulator.org >> > > > *CAUTION:* This email originated from outside the organization. Do not click links or open attachments unless you recognize the sender and know the content is safe. > > > _______________________________________________ > NEST Users mailing list -- users(a)nest-simulator.org > To unsubscribe send an email to users-leave(a)nest-simulator.org > > > *Attachments:* > • inhibition_score.pdf

2 1

Open NEST Developer Video Conference Monday March 9, at 11:30 CET (UTC+0100)
by Dennis Terhorst 09 Mar '26

09 Mar '26

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference today *Monday March 9, 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: /Hackathon updates/ o Recent changes to NEST o Replacing boost::any by C++20 variants The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2026-03-09-Open-NEST-Developer-… <https://github.com/nest/nest-simulator/wiki/2026-03-09-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/ --------------------------------------------------------------------------------------------- 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), Dr. Stephanie Bauer (stellvertretende Vorsitzende), Prof. Dr. Ir. Pieter Jansens, Prof. Dr. Laurens Kuipers ---------------------------------------------------------------------------------------------

1 0

Major changes (mostly behind the scenes) in NEST Simulator
by Hans Ekkehard Plesser 05 Mar '26

05 Mar '26

Dear NEST Users and Developers, This week has brought a range of important changes of NEST Simulator, mostly behind the scenes. Building on years of work, started by Jochen Eppler and Håkon Mørk, and a huge effort to port thousands of SLI-based tests to Python, we have finally removed the SLI interpreter from NEST. The Python interface now interacts directly with the C++ backend. These changes are currently only available to those who build NEST themselves form the source code on Github. We plan to release the first SLI-free NEST versions, NEST 3.10, ahead of the NEST Conference in June. Then, you will finally be able to pip install nest. For users, this should bring only minimal changes. You will find them documented on https://nest-simulator.readthedocs.io/en/latest/whats_new/v3.10/index.html#…. Please let us know if you run into any difficulties adjusting your model scripts. Developers may need to make some adjustments to your code, but also those changes should be rather schematic. The key points are described in the "What's new in 3.10" document linked above. Again, do not hesitate to contact us if you have any questions or run into problems. Finally, we have changed the name of the central branch for NEST Simulator from "master" to "main" on Github. So if you have NEST cloned to your computer from the public NEST repository, please do the following in your local repo, assuming that nest_public is the name for the remote git@github.com:nest/nest-simulator.git: git branch -m master main git fetch nest_public git branch -u nest_public/main main # do this only if you want to set nest_public/main as upstream tracking branch git remote set-head nest_public -a On behalf of the NEST developer crowd Hans Ekkehard -- Prof. Dr. Hans Ekkehard Plesser Research Committee Chair, Faculty of Science and Technology 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

1 0

NEST Conference 2026 Abstract Submission and Registration open
by Hans Ekkehard Plesser 05 Mar '26

05 Mar '26

Dear Colleagues, The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the NEST Conference 2026. 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. Take the opportunity to advance your skills in using NEST at our hands-on workshops! We explicitly encourage young scientists to participate in the conference! This year's conference will again take place as a virtual conference on Tuesday/Wednesday 16/17 June 2026. We are delighted to welcome * Marja-Leena Linne, Tampere University * Maxime Carriere and Fynn Dobler, Freie Universität Berlin * Agnes Korcsak-Gorzo, Forschungszentrum Jülich * Pablo Martínez Cañada, University of Granada as one of our keynote speakers at this year’s conference. Registration and submission of contributions are now open! We are inviting you to submit contributions in the form of talks and "posters". Please register and submit your contribution(s) via the conference website https://nest-simulator.org/conference. Important dates: 10 April 2026 - Deadline for submission of contributions 05 May 2026 - Notification of acceptance 09 June 2026 - Registration deadline We are looking forward to seeing you all in June! Hans Ekkehard Plesser and the conference organizing committee -- Prof. Dr. Hans Ekkehard Plesser Research Committee Chair, Faculty of Science and Technology 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

1 0

Heaviside function in a Kernel?
by Jeanne Barthelemy 05 Mar '26

05 Mar '26

Dear all, This is a question about best practices for NESTML. I would like to build in the equations block a Kernel, such as for instance: "kernel inh_time_end = ((-0.00002*t*t)/(pow(tau,2.0)) + 0.0087*t/tau + 0.006)/tau" , but modulated by the heaviside function H depending on 200 - t (i.e., H(200.0 ms -t)). To sum up, I would like to get the following kernel: "kernel inh_time_end_modulated = H(200.0 ms -t)*((-0.00002*t*t)/(pow(tau,2.0)) + 0.0087*t/tau + 0.006)/tau" There does not appear to be a direct implementation of the heaviside function H in nestml. I tried to build my own function to convert booleans to 1.0 or 0.0 in my NestML model, upon the condition "200.0 ms > t", and use that instead of the heaviside, but the model is really slow to build on my laptop. I tried other versions with if conditions in the update block, or ternary operators, but they all led to bugs. So, I am unsure of the correct practice to implement an heaviside-modulated polynomial kernel. If anyone has a good and fast solution to propose I would be very happy to learn about it ! Thank you very much, Kind regards, Jeanne BARTHELEMY

2 4

2026

2025

2024

2023

2022

2021

2020

2019

NEST Users March 2026