Neural Comput - Learning invariance from natural images inspired by observations in the primary visual cortex.

Tópicos

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{ first(2504) two(1366) second(1323) }
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{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
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{ analysi(2126) use(1163) compon(1037) }
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Resumo

The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.

Resumo Limpo

human visual system remark abil larg recogn object invari posit rotat scale good interpret neurobiolog find involv comput model simul signal process visual cortex part like achiev step step earli late area visual percept sever algorithm propos learn featur detector studi hand cover issu biolog plausibl learn invari studi set hebbian learn rule base calcium dynam homeostat regul singl neuron propos perform verifi within simpl model primari visual cortex learn socal complex cell base sequenc static imag result learn complexcel respons larg invari phase posit

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