Literature DB >> 12620162

Simple-cell-like receptive fields maximize temporal coherence in natural video.

Jarmo Hurri1, Aapo Hyvärinen.   

Abstract

Recently, statistical models of natural images have shown the emergence of several properties of the visual cortex. Most models have considered the nongaussian properties of static image patches, leading to sparse coding or independent component analysis. Here we consider the basic time dependencies of image sequences instead of their nongaussianity. We show that simple-cell-type receptive fields emerge when temporal response strength correlation is maximized for natural image sequences. Thus, temporal response strength correlation, which is a nonlinear measure of temporal coherence, provides an alternative to sparseness in modeling simple-cell receptive field properties. Our results also suggest an interpretation of simple cells in terms of invariant coding principles, which have previously been used to explain complex-cell receptive fields.

Mesh:

Year:  2003        PMID: 12620162     DOI: 10.1162/089976603321192121

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  9 in total

Review 1.  Mapping receptive fields in primary visual cortex.

Authors:  Dario L Ringach
Journal:  J Physiol       Date:  2004-05-21       Impact factor: 5.182

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

Authors:  Martin Rehn; Friedrich T Sommer
Journal:  J Comput Neurosci       Date:  2007-04       Impact factor: 1.621

3.  Emergence of complex cell properties by learning to generalize in natural scenes.

Authors:  Yan Karklin; Michael S Lewicki
Journal:  Nature       Date:  2008-11-19       Impact factor: 49.962

4.  Robustness of neural codes and its implication on natural image processing.

Authors:  Sheng Li; Si Wu
Journal:  Cogn Neurodyn       Date:  2007-07-12       Impact factor: 5.082

5.  How does the brain solve visual object recognition?

Authors:  James J DiCarlo; Davide Zoccolan; Nicole C Rust
Journal:  Neuron       Date:  2012-02-09       Impact factor: 17.173

6.  Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2.

Authors:  Aapo Hyvärinen; Michael Gutmann; Patrik O Hoyer
Journal:  BMC Neurosci       Date:  2005-02-16       Impact factor: 3.288

7.  Slowness and sparseness have diverging effects on complex cell learning.

Authors:  Jörn-Philipp Lies; Ralf M Häfner; Matthias Bethge
Journal:  PLoS Comput Biol       Date:  2014-03-06       Impact factor: 4.475

8.  Sustained firing of model central auditory neurons yields a discriminative spectro-temporal representation for natural sounds.

Authors:  Michael A Carlin; Mounya Elhilali
Journal:  PLoS Comput Biol       Date:  2013-03-28       Impact factor: 4.475

9.  Visual aftereffects and sensory nonlinearities from a single statistical framework.

Authors:  Valero Laparra; Jesús Malo
Journal:  Front Hum Neurosci       Date:  2015-10-13       Impact factor: 3.169

  9 in total

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