A histogram-based recursively trained classifier as a model for somatosensation

A simple, histogram-based pattern classifier can be used to model somatosensory cortical receptive field changes in monkeys after digit amputation. If a cortical neuron in an adult monkey has a receptive field on an amputated digit, the neuron´s receptive field initially becomes much larger after amputation, but is eventually restricted to a small area on the side of a neighboring digit. These observations can be explained by a classifier in which: (1) cortical neurons represent spatially ordered hypotheses that a stimulus is present in a given area of the hand; (2) the hypothesis made by each cortical neuron is unaffected by amputation or equivalent damage; (3) hypotheses are tested with a maximum likelihood, histogram-based classifier; (4) the classifier is trained and updated by recursive Hebbian learning; (5) imperfect exemplars are used to train the classifier; and (6) lateral inhibition exists between sensory sites. Implications of the model for prosthesis are suggested