Neural Comput - Effects of multiplicative power law neural noise in visual information processing.

Tópicos

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Resumo

The human visual system is intrinsically noisy. The benefits of internal noise as part of visual code are controversial. Here the information-theoretic properties of multiplicative (i.e. signal-dependent) neural noise are investigated. A quasi-linear communication channel model is presented. The model shows that multiplicative power law neural noise promotes the minimum information transfer after efficient coding. It is demonstrated that Weber's law and the human contrast sensitivity function arise on the basis of minimum transfer of information and power law neural noise. The implications of minimum information transfer in self-organized neural networks and weakly coupled neurons are discussed.

Resumo Limpo

human visual system intrins noisi benefit intern nois part visual code controversi informationtheoret properti multipl ie signaldepend neural nois investig quasilinear communic channel model present model show multipl power law neural nois promot minimum inform transfer effici code demonstr weber law human contrast sensit function aris basi minimum transfer inform power law neural nois implic minimum inform transfer selforgan neural network weak coupl neuron discuss

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