Storage capacity of attractor neural networks with depressing synapses.
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.
Torres
Joaquín J
JJ
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
Pantic
Lovorka
L
Kappen
Hilbert J
HJ