Symmetric sequence processing in a recurrent neural network model with a synchronous dynamics
arXiv:0908.1547 · doi:10.1088/1751-8113/42/38/385001
Abstract
The synchronous dynamics and the stationary states of a recurrent attractor neural network model with competing synapses between symmetric sequence processing and Hebbian pattern reconstruction is studied in this work allowing for the presence of a self-interaction for each unit. Phase diagrams of stationary states are obtained exhibiting phases of retrieval, symmetric and period-two cyclic states as well as correlated and frozen-in states, in the absence of noise. The frozen-in states are destabilised by synaptic noise and well separated regions of correlated and cyclic states are obtained. Excitatory or inhibitory self-interactions yield enlarged phases of fixed-point or cyclic behaviour.
Accepted for publication in Journal of Physics A: Mathematical and Theoretical
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