Stochastic learning in a neural network with adapting synapses

Details Stochastic learning in a neural network with adapting synapses Stochastic learning in a neural network with adapting synapses

1997-05-19

arxiv.org/abs/cond-mat/9705182v1

Physics

Condensed Matter

Disordered Systems and Neural Networks

25 pages, LaTeX file

Scientific paper

10.1103/PhysRevE.56.4567

We consider a neural network with adapting synapses whose dynamics can be analitically computed. The model is made of $N$ neurons and each of them is connected to $K$ input neurons chosen at random in the network. The synapses are $n$-states variables which evolve in time according to Stochastic Learning rules; a parallel stochastic dynamics is assumed for neurons. Since the network maintains the same dynamics whether it is engaged in computation or in learning new memories, a very low probability of synaptic transitions is assumed. In the limit $N\to\infty$ with $K$ large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analitically calculated by flow equations for the macroscopic parameters of the system.

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