Work

Storage capacity of attractor neural networks with depressing synapses

Year: 2002

Type: article

Abstract: We compute the capacity of a binary neural network with dynamic depressing synapses to store and retrieve an infinite number of patterns. We use a biologically motivated model of synaptic depression and a standard mean-field approach. We find that at T=0 the critical storage capacity decreases with the degree of the depression. We confirm the validity of our main mean-field results with numerical simulations.

Source: Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics

DOI: 10.1103/physreve.66.061910

Authors: Joaquín J. Torres, Lovorka Pantic, Hilbert J. Kappen

Institutions: Universidad de Granada, Radboud University Nijmegen

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2110163023

FWCI: 1.477

Cites: 14

Cited by: 32

Related to: 10