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Updated: Aug 6, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Storage capacity of attractor neural networks with depressing synapses
Joaquín J Torres1, Lovorka Pantic, Hilbert J Kappen
1Institute "Carlos I" for Theoretical and Computational Physics, Department of Electromagnetism and Material Physics, University of Granada, E-18071 Granada, Spain. jtorres@onsager.ugr.es
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.
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