We consider a neural network with adapting synapses whose dynamics can be analytically 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-state variables that 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→ with K large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analytically calculated by flow equations for the macroscopic parameters of the system.

Stochastic learning in a neural network with adapting synapses

LATTANZI, GIANLUCA;STRAMAGLIA, Sebastiano
1997-01-01

Abstract

We consider a neural network with adapting synapses whose dynamics can be analytically 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-state variables that 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→ with K large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analytically calculated by flow equations for the macroscopic parameters of the system.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/126547
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