Emergent rate-based dynamics in duplicate-free populations of spiking neurons

Fuente: arXiv
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Main Authors: Schmutz, Valentin, Brea, Johanni, Gerstner, Wulfram
Format: Preprint
Published: 2023
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author Schmutz, Valentin
Brea, Johanni
Gerstner, Wulfram
author_facet Schmutz, Valentin
Brea, Johanni
Gerstner, Wulfram
contents Can Spiking Neural Networks (SNNs) approximate the dynamics of Recurrent Neural Networks (RNNs)? Arguments in classical mean-field theory based on laws of large numbers provide a positive answer when each neuron in the network has many "duplicates", i.e. other neurons with almost perfectly correlated inputs. Using a disordered network model that guarantees the absence of duplicates, we show that duplicate-free SNNs can converge to RNNs, thanks to the concentration of measure phenomenon. This result reveals a general mechanism underlying the emergence of rate-based dynamics in large SNNs.
format Preprint
id arxiv_https___arxiv_org_abs_2303_05174
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Emergent rate-based dynamics in duplicate-free populations of spiking neurons
Schmutz, Valentin
Brea, Johanni
Gerstner, Wulfram
Neurons and Cognition
Can Spiking Neural Networks (SNNs) approximate the dynamics of Recurrent Neural Networks (RNNs)? Arguments in classical mean-field theory based on laws of large numbers provide a positive answer when each neuron in the network has many "duplicates", i.e. other neurons with almost perfectly correlated inputs. Using a disordered network model that guarantees the absence of duplicates, we show that duplicate-free SNNs can converge to RNNs, thanks to the concentration of measure phenomenon. This result reveals a general mechanism underlying the emergence of rate-based dynamics in large SNNs.
title Emergent rate-based dynamics in duplicate-free populations of spiking neurons
topic Neurons and Cognition
url https://arxiv.org/abs/2303.05174