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

Authors: Younes Bouhadjar; Dirk J. Wouters; Markus Diesmann; Tom Tetzlaff;

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

Abstract

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

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).

Keywords

sequence learning, temporal memory, synaptic plasticity, homeostasis, spiking neurons, dendritic action potential

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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