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Optimal signal representation in neural spiking codes: A model for the formation of simple cell receptive fields.

Abstract : Taking advantage of the constraints of spiking representations, we derive an unsupervised learning algorithm solving efficiently the inverse problem of pattern matching and apply it to a model of the input to the primary visual cortex. In fact, spikes carry temporal event-based information in bundles of parallel fibers and may be considered as all-or-none binary events. This property may be used to formulate the efficiency of a representation problem as finding the L$_0$-norm sparsest representation, a ``hard" NP-complete problem. This framework improves previous results based on an Adaptive Matching Pursuit scheme by explicitly implementing an homeostatic constraint in the choice function by a spiking gain control mechanism in the neural population. For comparison purposes, we applied this scheme to the learning of small images taken from natural images as in SparseNet and compared the results and efficiency of this last algorithm with Matching Pursuit and the proposed algorithm. This study provides a generic algorithm for learning independent components in a set of inputs such as natural images suggesting that this Sparse Spike Coding strategy may provide a generic computational module that help us understanding the efficiency of the Primary Visual Cortex.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-00156610
Contributor : Laurent Perrinet Connect in order to contact the contributor
Submitted on : Tuesday, February 5, 2008 - 8:28:30 PM
Last modification on : Thursday, January 11, 2018 - 6:16:39 AM
Long-term archiving on: : Thursday, September 23, 2010 - 5:07:19 PM

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  • HAL Id : hal-00156610, version 3
  • ARXIV : 0706.3177

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Laurent Perrinet. Optimal signal representation in neural spiking codes: A model for the formation of simple cell receptive fields.. 2008. ⟨hal-00156610v3⟩

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