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Attractor neural networks with double well synapses.

Publication ,  Journal Article
Feng, Y; Brunel, N
Published in: PLoS Comput Biol
February 2024

It is widely believed that memory storage depends on activity-dependent synaptic modifications. Classical studies of learning and memory in neural networks describe synaptic efficacy either as continuous or discrete. However, recent results suggest an intermediate scenario in which synaptic efficacy can be described by a continuous variable, but whose distribution is peaked around a small set of discrete values. Motivated by these results, we explored a model in which each synapse is described by a continuous variable that evolves in a potential with multiple minima. External inputs to the network can switch synapses from one potential well to another. Our analytical and numerical results show that this model can interpolate between models with discrete synapses which correspond to the deep potential limit, and models in which synapses evolve in a single quadratic potential. We find that the storage capacity of the network with double well synapses exhibits a power law dependence on the network size, rather than the logarithmic dependence observed in models with single well synapses. In addition, synapses with deeper potential wells lead to more robust information storage in the presence of noise. When memories are sparsely encoded, the scaling of the capacity with network size is similar to previously studied network models in the sparse coding limit.

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Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

February 2024

Volume

20

Issue

2

Start / End Page

e1011354

Location

United States

Related Subject Headings

  • Synapses
  • Neural Networks, Computer
  • Models, Neurological
  • Memory
  • Learning
  • Bioinformatics
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences
 

Citation

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Feng, Y., & Brunel, N. (2024). Attractor neural networks with double well synapses. PLoS Comput Biol, 20(2), e1011354. https://doi.org/10.1371/journal.pcbi.1011354
Feng, Yu, and Nicolas Brunel. “Attractor neural networks with double well synapses.PLoS Comput Biol 20, no. 2 (February 2024): e1011354. https://doi.org/10.1371/journal.pcbi.1011354.
Feng Y, Brunel N. Attractor neural networks with double well synapses. PLoS Comput Biol. 2024 Feb;20(2):e1011354.
Feng, Yu, and Nicolas Brunel. “Attractor neural networks with double well synapses.PLoS Comput Biol, vol. 20, no. 2, Feb. 2024, p. e1011354. Pubmed, doi:10.1371/journal.pcbi.1011354.
Feng Y, Brunel N. Attractor neural networks with double well synapses. PLoS Comput Biol. 2024 Feb;20(2):e1011354.

Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

February 2024

Volume

20

Issue

2

Start / End Page

e1011354

Location

United States

Related Subject Headings

  • Synapses
  • Neural Networks, Computer
  • Models, Neurological
  • Memory
  • Learning
  • Bioinformatics
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences