Inhibition Delay Increases Neural Network Capacity through Stirling Transform

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Abstract

Inhibitory neural networks are found to encode high volumes of information through delayed inhibition. We show that inhibition delay increases storage capacity through a Stirling transform of the minimum capacity which stabilizes locally coherent oscillations. We obtain both the exact and asymptotic formulas for the total number of dynamic attractors. Our results predict a (ln2)-N-fold increase in capacity for an N-neuron network and demonstrate high-density associative memories which host a maximum number of oscillations in analog neural devices.

Original languageEnglish
Article number030301(R)
Pages (from-to)1-4
Number of pages4
JournalPhysical Review E
Volume97
Issue number3
Early online date9 Mar 2018
DOIs
Publication statusPublished - 9 Mar 2018

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Neural Networks
Transform
Associative Memory
Storage Capacity
Asymptotic Formula
Attractor
associative memory
Neuron
Fold
Oscillation
neurons
Predict
Demonstrate
oscillations

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Cite this

Inhibition Delay Increases Neural Network Capacity through Stirling Transform. / Nogaret, Alain; King, Alastair.

In: Physical Review E, Vol. 97, No. 3, 030301(R), 09.03.2018, p. 1-4.

Research output: Contribution to journalArticle

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