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Biomedical subjects

P Dayan

Publications and source records attributed to P Dayan.

20 records · Page 2Linked to original sources

A correlational model for the development of disparity selectivity in visual cortex that depends on prenatal and postnatal phases.

Neurons in the visual cortex require correlated binocular activity during a critical period early in life to develop normal response properties. We present a model for how the disparity selectivity of cortical neurons might arise during development. The model is based on Hebbian mechanisms for plasticity at synapses between geniculocortical neurons and cortical cells. The model is driven by correlated activity in retinal ganglion cells within each eye before birth and additionally between eyes after birth. With no correlations present between the eyes, the cortical model developed only monocular cells. Adding a small amount of correlation between eyes at the beginning of development produced cortical neurons that were entirely binocular and tuned to zero disparity. However, if an initial phase of purely same-eye correlations was followed by a second phase of development that included correlations between eyes, the cortical model became populated with both monocular and binocular cells. Moreover, in the two-phase model, binocular cells tended to be selective for zero disparity, whereas the more monocular cells tended to have nonzero disparity. This relationship between ocular dominance and disparity has been observed in the visual cortex of the cat by other workers. Differences in the relative timing of the two developmental phases could account for the higher proportion of monocular cells found in the visual cortices of other animals.

Aging↗

Optimising synaptic learning rules in linear associative memories.

Associative matrix memories with real-valued synapses have been studied in many incarnations. We consider how the signal/noise ratio for associations depends on the form of the learning rule, and we show that a covariance rule is optimal. Two other rules, which have been suggested in the neurobiology literature, are asymptotically optimal in the limit of sparse coding. The results appear to contradict a line of reasoning particularly prevalent in the physics community. It turns out that the apparent conflict is due to the adoption of different underlying models. Ironically, they perform identically at their co-incident optima. We give details of the mathematical results, and discuss some other possible derivations and definitions of the signal/noise ratio.

Animals↗