ABSTRACT
A generalized version of Amari’s field-theoretic approach to neural networks is used to model a situation in which the weights of the recurrent synapses across a two-dimensional neural field vary with time according to a Hebbian type law. Assuming equilibrium, an equation relating the synaptic weight function to the neuronal receptive field overlap derived. It is studied in a simple case in which the weights are isotropic and symmetric.
