title: Storage capacity of attractor neural networks with depressing synapses.
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.
authors.affiliation: Institute "Carlos I" for Theoretical and Computational Physics, Department of Electromagnetism and Material Physics, University of Granada, E-18071 Granada, Spain. jtorres@onsager.ugr.es
authors.forename: Joaquín J
authors.initials: JJ
authors.lastname: Torres
authors.forename: Lovorka
authors.initials: L
authors.lastname: Pantic
authors.forename: Hilbert J
authors.initials: HJ
authors.lastname: Kappen