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  4. Emergent Rate-Based Dynamics in Duplicate-Free Populations of Spiking Neurons
 
research article

Emergent Rate-Based Dynamics in Duplicate-Free Populations of Spiking Neurons

Schmutz, Valentin  
•
Brea, Johanni  
•
Gerstner, Wulfram  
January 6, 2025
Physical Review Letters

Can spiking neural networks (SNNs) approximate the dynamics of recurrent neural networks? 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 recurrent neural networks, thanks to the concentration of measure phenomenon. This result reveals a general mechanism underlying the emergence of rate-based dynamics in large SNNs.

            Published by the American Physical Society
            2025
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Type
research article
DOI
10.1103/PhysRevLett.134.018401
Author(s)
Schmutz, Valentin  
Brea, Johanni  

EPFL

Gerstner, Wulfram  

EPFL

Date Issued

2025-01-06

Publisher

American Physical Society (APS)

Published in
Physical Review Letters
Volume

134

Issue

1

Article Number

018401

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LCN1  
FunderFunding(s)Grant NumberGrant URL

Swiss National Science Foundation

200020_207426

Available on Infoscience
January 9, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/242670
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