Neural Comput - Dependency reduction with divisive normalization: justification and effectiveness.

Tópicos

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Resumo

Efficient coding transforms that reduce or remove statistical dependencies in natural sensory signals are important for both biology and engineering. In recent years, divisive normalization (DN) has been advocated as a simple and effective nonlinear efficient coding transform. In this work, we first elaborate on the theoretical justification for DN as an efficient coding transform. Specifically, we use the multivariate t model to represent several important statistical properties of natural sensory signals and show that DN approximates the optimal transforms that eliminate statistical dependencies in the multivariate t model. Second, we show that several forms of DN used in the literature are equivalent in their effects as efficient coding transforms. Third, we provide a quantitative evaluation of the overall dependency reduction performance of DN for both the multivariate t models and natural sensory signals. Finally, we find that statistical dependencies in the multivariate t model and natural sensory signals are increased by the DN transform with low-input dimensions. This implies that for DN to be an effective efficient coding transform, it has to pool over a sufficiently large number of inputs.

Resumo Limpo

effici code transform reduc remov statist depend natur sensori signal import biolog engin recent year divis normal dn advoc simpl effect nonlinear effici code transform work first elabor theoret justif dn effici code transform specif use multivari t model repres sever import statist properti natur sensori signal show dn approxim optim transform elimin statist depend multivari t model second show sever form dn use literatur equival effect effici code transform third provid quantit evalu overal depend reduct perform dn multivari t model natur sensori signal final find statist depend multivari t model natur sensori signal increas dn transform lowinput dimens impli dn effect effici code transform pool suffici larg number input

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