Learning invariant responses to the natural transformations of objects

The primate visual system builds representations of objects which are invariant with respect to transforms such as translation, size, and eventually view, in a series of hierarchical cortical areas. To clarify how such a system might learn to recognise 'naturally' transformed objects, we are investigating a model of cortical visual processing which incorporates a, number of features of the primate visual system. The model has a series of layers with convergence from a limited region of the preceding layer, and mutual inhibition over a short range within a layer. The feedforward connections between layers provide the inputs to competitive networks, each utilising a modified Hebb-like learning rule which incorporates a temporal trace of the preceding neuronal activity. The trace learning rule is aimed at enabling the neurons to learn transform invariant responses via experience of the real world, with its inherent spatio-temporal constraints. We show that the model can learn to produce translation-invariant responses.