Invariant visual object and face recognition: neural and computational bases, and a model, VisNet

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy modelin which invariant representations can be built by self-organizing learning based on the temporal and spatialstatistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associativesynaptic learning rule with a short term memory trace, and/or it can use spatialcontinuity in Continuous Spatial Transformation learning which does not require a temporal trace. The model of visual processing in theventral cortical stream can build representations of objects that are invariant withrespect to translation, view, size, and also lighting. The modelhas been extended to provide an account of invariant representations in the dorsal visualsystem of the global motion produced by objects such as looming, rotation, and objectbased movement. The model has been extended to incorporate top-down feedback connectionsto model the control of attention by biased competition in for example spatial and objectsearch tasks. The model has also been extended to account for how the visual system canselect single objects in complex visual scenes, and how multiple objects can berepresented in a scene. The model has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.