DYNAMICS OF A RANDOM NEURAL-NETWORK WITH SYNAPTIC DEPRESSION

We consider a randomly connected neural network with linear threshold elements which update in discrete time steps. The two main features of the network are: (I) equally distributed and purely excitatory connec tions and (2) synaptic depression after repetitive firing. We focus on the time evolution of the expected network activity. The four types o f qualitative behavior are investigated: singular excitation, converge nce to a constant activity, oscillation, and chaos. Their occurrence i s discussed as a function of the average number of connections and the synaptic depression time. Our model relies on experiments with a slic e culture of disinhibited embryonic rat spinal cord. The dynamics of t hese networks essentially depends on the following characteristics: th e low non-structured connectivity, the high synaptic depression time a nd the large EPSP with respect to the threshold value. Copyright (C) 1 996 Elsevier Science Ltd