Synaptic depression in associative memory networks

We analyze the effects of synaptic depression on the stability of patterns stored in neural networks with low activity level. Applying mean-field theory we show that the stationary states remain unaffected by the synaptic depression. However the stability of memory patterns changes drastically causing a reduction of memory capacity. Further, it is demonstrated and confirmed by numerical calculations that the sensitivity of the network to input changes is enhanced.

[1]  Idan Segev,et al.  Reading Neuronal Synchrony with Depressing Synapses , 1998, Neural Computation.

[2]  Sompolinsky,et al.  Spin-glass models of neural networks. , 1985, Physical review. A, General physics.

[3]  P C Bressloff,et al.  Mean-field theory of globally coupled integrate-and-fire neural oscillators with dynamic synapses. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[4]  Adam Prügel-Bennett,et al.  Analysis of synfire chains , 1995 .

[5]  M. Tsodyks,et al.  The Enhanced Storage Capacity in Neural Networks with Low Activity Level , 1988 .

[6]  J. Leo van Hemmen,et al.  Short-Term Synaptic Plasticity and Network Behavior , 1999, Neural Computation.

[7]  L. Abbott,et al.  Synaptic Depression and Cortical Gain Control , 1997, Science.

[8]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Henry Markram,et al.  Neural Networks with Dynamic Synapses , 1998, Neural Computation.