Learning generalisation and localisation: Competition for stimulus type and receptive field

The evidence from neurophysiological recordings from the primate visual system suggests that sensory patterns are processed using units arranged in a hierarchical multi-layered network. Responses of these units show progressively increasing receptive field size combined with selectivity for increasing stimulus complexity at successively higher levels. It is argued that the rate of the increase in receptive field size is less than the maximum possible given the initial spread of neuronal projections that occurs during development. We show here that a competitive learning mechanism using a 'trace-Hebbian' learning rule [14] with a larger number of competing output units learns not only positional invariance for a given input feature but can also establish restricted receptive field sizes (i.e. less than the maximum size given the initial connections). Importantly the same stimulus selectivity was maintained throughout the receptive field. It is shown that this is accompanied by a relative increase in the spatial evenness of the representation of each detector type across position within the input array. The network properties were found to be robust and stable over a wide range of learning parameters. We suggest that such a competitive mechanism may help account for the reported properties of cells in the ventral stream of the primate visual system.