V1 non-linear properties emerge from local-to-global non-linear ICA

Network : Computation in Neural Systems
Jesús Malo, Juan Gutiérrez

Abstract

It has been argued that the aim of non-linearities in different visual and auditory mechanisms may be to remove the relations between the coefficients of the signal after global linear ICA-like stages. Specifically, in Schwartz and Simoncelli (2001), it was shown that masking effects are reproduced by fitting the parameters of a particular non-linearity in order to remove the dependencies between the energy of wavelet coefficients. In this work, we present a different result that supports the same efficient encoding hypothesis. However, this result is more general because, instead of assuming any specific functional form for the non-linearity, we show that by using an unconstrained approach, masking-like behavior emerges directly from natural images. This result is an additional indication that Barlow's efficient encoding hypothesis may explain not only the shape of receptive fields of V1 sensors but also their non-linear behavior.

References

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Citations

Aug 1, 2012·Neural Computation·Valero LaparraJesús Malo
Mar 8, 2012·PLoS Computational Biology·Ruben Coen-CagliOdelia Schwartz
Apr 3, 2010·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Valero LaparraJesús Malo
Nov 4, 2015·Frontiers in Human Neuroscience·Valero Laparra, Jesús Malo
Oct 16, 2018·PloS One·M Martinez-GarciaJ Malo
Mar 22, 2019·Frontiers in Neuroscience·Marina Martinez-GarciaJesús Malo
Nov 12, 2020·Journal of Mathematical Neuroscience·Jesús Malo

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