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Engineering-approach accelerates computational understanding of V1–V2 neural properties

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We present two computational models (i) long-range horizontal connections and the nonlinear effect in V1 and (ii) the filling-in process at the blind spot. Both models are obtained deductively from standard regularization theory to show that physiological evidence of V1 and V2 neural properties is essential for efficient image processing. We stress that the engineering approach should be imported to understand visual systems computationally, even though this approach usually ignores physiological evidence and the target is neither neurons nor the brain.

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  1. \(W_{\theta\rho}({\user2{x}}{^\prime})\) is decomposed into two Gaussian functions: \(G^{\text{exc}}_{\theta\rho}({\user2{x}}{^\prime})-G^{\text{inh}}_{\theta\rho}({\user2{x}}{^\prime}),\) also known as the DoG function. Figure 2c is a plot of \(\int{\text{d}}{\user2{x}}{^\prime}\{G^{\text{exc}}_{\theta\rho}({\user2{x}}{^\prime})+G^{\text{inh}}_{\theta\rho}({\user2{x}}{^\prime})\}.\)


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This work was partially supported by a Grant-in-Aid for Young Scientists (#17700244 and #20700279) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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Correspondence to Shunji Satoh.

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Satoh, S., Usui, S. Engineering-approach accelerates computational understanding of V1–V2 neural properties. Cogn Neurodyn 3, 1–8 (2009).

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