Efficient sparse coding in early sensory processing: lessons from signal recovery

Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representa...

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Bibliographic Details
Published in:PLoS computational biology 2012-03, Vol.8 (3), p.e1002372-e1002372
Main Authors: Lörincz, András, Palotai, Zsolt, Szirtes, Gábor
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Anatomy & physiology
Animals
Biology
Codes
Coding theory
Computational biology
Computer Simulation
Decomposition
Experiments
Humans
Models, Neurological
Nerve Net - physiology
Optimization algorithms
Physiological aspects
Principal components analysis
Retina - physiology
Sensory Receptor Cells - physiology
Sensory receptors
Sparsity
Stimuli (Psychology)
Studies
Visual Cortex - physiology
Visual Perception - physiology
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Summary:Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representations in an efficient way. We argue that higher level overcompleteness becomes computationally tractable by imposing sparsity on synaptic activity and we also show that such structural sparsity can be facilitated by statistics based decomposition of the stimuli into typical and atypical parts prior to sparse coding. Typical parts represent large-scale correlations, thus they can be significantly compressed. Atypical parts, on the other hand, represent local features and are the subjects of actual sparse coding. When applied on natural images, our decomposition based sparse coding model can efficiently form overcomplete codes and both center-surround and oriented filters are obtained similar to those observed in the retina and the primary visual cortex, respectively. Therefore we hypothesize that the proposed computational architecture can be seen as a coherent functional model of the first stages of sensory coding in early vision.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1002372