Literature DB >> 16275045

Temporal codes and sparse representations: a key to understanding rapid processing in the visual system.

Rudy Guyonneau1, Rufin Vanrullen, Simon J Thorpe.   

Abstract

Where neural information processing is concerned, there is no debate about the fact that spikes are the basic currency for transmitting information between neurons. How the brain actually uses them to encode information remains more controversial. It is commonly assumed that neuronal firing rate is the key variable, but the speed with which images can be analysed by the visual system poses a major challenge for rate-based approaches. We will thus expose here the possibility that the brain makes use of the spatio-temporal structure of spike patterns to encode information. We then consider how such rapid selective neural responses can be generated rapidly through spike-timing-dependent plasticity (STDP) and how these selectivities can be used for visual representation and recognition. Finally, we show how temporal codes and sparse representations may very well arise one from another and explain some of the remarkable features of processing in the visual system.

Mesh:

Year:  2005        PMID: 16275045     DOI: 10.1016/j.jphysparis.2005.09.004

Source DB:  PubMed          Journal:  J Physiol Paris        ISSN: 0928-4257


  13 in total

1.  Generalization of learning by synchronous waves: from perceptual organization to invariant organization.

Authors:  David M Alexander; Chris Trengove; Phillip E Sheridan; Cees van Leeuwen
Journal:  Cogn Neurodyn       Date:  2010-12-10       Impact factor: 5.082

2.  The power of the feed-forward sweep.

Authors:  Rufin Vanrullen
Journal:  Adv Cogn Psychol       Date:  2008-07-15

3.  Spectral analysis of input spike trains by spike-timing-dependent plasticity.

Authors:  Matthieu Gilson; Tomoki Fukai; Anthony N Burkitt
Journal:  PLoS Comput Biol       Date:  2012-07-05       Impact factor: 4.475

4.  Attention influences single unit and local field potential response latencies in visual cortical area V4.

Authors:  Kristy A Sundberg; Jude F Mitchell; Timothy J Gawne; John H Reynolds
Journal:  J Neurosci       Date:  2012-11-07       Impact factor: 6.167

5.  On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex.

Authors:  Carlos Zamarreño-Ramos; Luis A Camuñas-Mesa; Jose A Pérez-Carrasco; Timothée Masquelier; Teresa Serrano-Gotarredona; Bernabé Linares-Barranco
Journal:  Front Neurosci       Date:  2011-03-17       Impact factor: 4.677

6.  Recurrent Processing during Object Recognition.

Authors:  Randall C O'Reilly; Dean Wyatte; Seth Herd; Brian Mingus; David J Jilk
Journal:  Front Psychol       Date:  2013-04-01

7.  Order-based representation in random networks of cortical neurons.

Authors:  Goded Shahaf; Danny Eytan; Asaf Gal; Einat Kermany; Vladimir Lyakhov; Christoph Zrenner; Shimon Marom
Journal:  PLoS Comput Biol       Date:  2008-11-21       Impact factor: 4.475

8.  Event-related potentials reveal rapid verification of predicted visual input.

Authors:  Michael Dambacher; Martin Rolfs; Kristin Göllner; Reinhold Kliegl; Arthur M Jacobs
Journal:  PLoS One       Date:  2009-03-31       Impact factor: 3.240

9.  Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains.

Authors:  Timothée Masquelier; Rudy Guyonneau; Simon J Thorpe
Journal:  PLoS One       Date:  2008-01-02       Impact factor: 3.240

10.  Sparse representation of sounds in the unanesthetized auditory cortex.

Authors:  Tomás Hromádka; Michael R Deweese; Anthony M Zador
Journal:  PLoS Biol       Date:  2008-01       Impact factor: 8.029

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