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Abstract:
Gain control by divisive inhibition, a.k.a. Heeger's
normalization, seems to be a general mechanism throughout the
visual cortex. We explore in this study the statistical properties
of this normalization in the presence of noise. Using simulations,
we show that Heeger's normalization is a close approximation to a
maximum likelihood estimator, which, in the context of population
coding, is the same as an ideal observer. We also demonstrate
analytically that this is a general property of a large class of
nonlinear recurrent networks with line attractors. Our work
suggests that Heeger's normalization plays a critical role in noise
filtering, and that every cortical layer may be an ideal observer
of the activity in the preceding layer.
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