Contribution to a conference proceedings

FZJ-2024-01911

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Attractor states in spiking neural networks

Ebrahimzadeh, p. (Corresponding author)FZJ* ; Bouhadjar, Y.FZJ* ; Schiek, M.FZJ* ; Strachan, J. P.FZJ* ; Neftci, E.FZJ*

2023
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Please use a persistent id in citations: doi:10.12751/NNCN.BC2023.174

Abstract: Considering the brain as a dynamical system allows for a rigorous analysis of some aspects of brain dynamics regarding the working memory and their relation to spike patterns. The concept of attractor networks describes one of the dynamical mechanisms in which the brain maintains persistent activity via creating locally stable attractor states. In this work, we analyze the interplay between the excitatory\inhibitory electrical and chemical synaptic connections as means of creation [and annihilation] mechanism of different dynamical regimes and their respective attractor states considering a network of spiking neurons. The exponentially-decaying external input is introduced as means of a vector field driving the system into a specific attractor. Based on the topological analysis of the state space of the input-driven system, computational capabilities of the attractor states and link to working memory are discussed.

Keyword(s): Computational Neuroscience ; Networks and dynamical systems

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Contributing Institute(s):

1. Neuromorphic Compute Nodes (PGI-14)

Research Program(s):

1. 5234 - Emerging NC Architectures (POF4-523) (POF4-523)

Appears in the scientific report 2024

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