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Journal of Computational Neuroscience

Erschienen in:

01.04.2007

A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields

verfasst von: Martin Rehn, Friedrich T. Sommer

Erschienen in: Journal of Computational Neuroscience | Ausgabe 2/2007

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Abstract

Computational models of primary visual cortex have demonstrated that principles of efficient coding and neuronal sparseness can explain the emergence of neurones with localised oriented receptive fields. Yet, existing models have failed to predict the diverse shapes of receptive fields that occur in nature. The existing models used a particular “soft” form of sparseness that limits average neuronal activity. Here we study models of efficient coding in a broader context by comparing soft and “hard” forms of neuronal sparseness.

As a result of our analyses, we propose a novel network model for visual cortex. The model forms efficient visual representations in which the number of active neurones, rather than mean neuronal activity, is limited. This form of hard sparseness also economises cortical resources like synaptic memory and metabolic energy. Furthermore, our model accurately predicts the distribution of receptive field shapes found in the primary visual cortex of cat and monkey.

Vorheriger Artikel STDP rule endowed with the BCM sliding threshold accounts for hippocampal heterosynaptic plasticity

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For an introduction to these models, see chapter ten of Dayan and Abbott (2003).

The distribution of shapes of receptive fields in the primary visual cortex of cat and monkey are very similar, for a comparison see Ringach (2002).

Note that in the Sparsenet, even though the combination of feedforward and feedback is linear the coding is ultimately a nonlinear operation on the input because feedback is present.

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Titel: A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields
verfasst von: Martin Rehn
Friedrich T. Sommer
Publikationsdatum: 01.04.2007
Verlag: Kluwer Academic Publishers-Plenum Publishers
Erschienen in: Journal of Computational Neuroscience / Ausgabe 2/2007
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-006-0003-9

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