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- W3088391290 abstract "Canonical vision models of the retina-to-V1 cortex pathway consist of cascades of several Linear+Nonlinear layers. In this setting, parameter tuning is the key to obtain a sensible behavior when putting all these multiple layers to work together. Conventional tuning of these neural models very much depends on the explicit computation of the derivatives of the response with regard to the parameters. And, in general, this is not an easy task. Automatic differentiation is a tool developed by the deep learning community to solve similar problems without the need of explicit computation of the analytic derivatives. Therefore, implementations of canonical visual neuroscience models that are ready to be used in an automatic differentiation environment are extremely needed nowadays. In this work we introduce a Python implementation of a standard multi-layer model for the retina-to-V1 pathway. Results show that the proposed default parameters reproduce image distortion psychophysics. More interestingly, given the python implementation, the parameters of this visual model are ready to be optimized with automatic differentiation tools for alternative goals." @default.
- W3088391290 created "2020-10-01" @default.
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- W3088391290 date "2020-01-01" @default.
- W3088391290 modified "2023-09-26" @default.
- W3088391290 title "Canonical Retina-to-Cortex Vision Model Ready for Automatic Differentiation" @default.
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- W3088391290 doi "https://doi.org/10.1007/978-3-030-59277-6_30" @default.
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