Published July 29, 2022 | Version 1.1
Software Open

Sequence learning, prediction, and replay in networks of spiking neurons

Authors/Creators

  • 1. Institute of Neuroscience and Medicine (INM-6) & Institute for Advanced Simulation (IAS-6) & JARA Institute Brain Structure-Function Relationships(INM-10), Jülich Research Centre, Jülich, Germany. Peter Grünberg Institute (PGI-7,10), Jülich Research Centre and JARA, Jülich, Germany. RWTH Aachen University, Aachen, Germany
  • 2. Institute of Electronic Materials (IWE 2) & JARA-FIT, RWTH Aachen University, Aachen, Germany
  • 3. Institute of Neuroscience and Medicine (INM-6) & Institute for Advanced Simulation (IAS-6) & JARA Institute Brain Structure-Function Relationships(INM-10), Jülich Research Centre, Jülich, Germany. Department of Physics, Faculty 1, & Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
  • 4. Institute of Neuroscience and Medicine (INM-6) & Institute for Advanced Simulation (IAS-6) & JARA Institute Brain Structure-Function Relationships(INM-10), Jülich Research Centre, Jülich, Germany

Description

This repository contains source code and simulation scripts to perform the experiments as well as the data analysis to reproduce the figures shown in:

Bouhadjar Y, Wouters DJ, Diesmann M, Tetzlaff T (2022), Sequence learning, prediction, and replay in networks of spiking neurons. PLOS Computational Biology, 18(6), e1010233.

Details can be found in the included README.md
Note: when building the NESTML model, install this version of the anrlr4 package: pip install antlr4-python3-runtime==4.9.2

Notes

This project was funded by the Helmholtz Association Initiative and Networking Fund (project number SO-092, Advanced Computing Architectures), and the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3). Open access publication funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, 491111487).

Files

Bouhadjar_et_al_2021-1.1.zip

Files (428.6 MB)

Name Size Download all
md5:53440ef7f16bff18ff697bab5c02a793
428.6 MB Preview Download