Literature DB >> 17283516

Theoretical understanding of the early visual processes by data compression and data selection.

Li Zhaoping1.   

Abstract

Early vision is best understood in terms of two key information bottlenecks along the visual pathway -- the optic nerve and, more severely, attention. Two effective strategies for sampling and representing visual inputs in the light of the bottlenecks are (1) data compression with minimum information loss and (2) data deletion. This paper reviews two lines of theoretical work which understand processes in retina and primary visual cortex (V1) in this framework. The first is an efficient coding principle which argues that early visual processes compress input into a more efficient form to transmit as much information as possible through channels of limited capacity. It can explain the properties of visual sampling and the nature of the receptive fields of retina and V1. It has also been argued to reveal the independent causes of the inputs. The second theoretical tack is the hypothesis that neural activities in V1 represent the bottom up saliencies of visual inputs, such that information can be selected for, or discarded from, detailed or attentive processing. This theory links V1 physiology with pre-attentive visual selection behavior. By making experimentally testable predictions, the potentials and limitations of both sets of theories can be explored.

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Year:  2006        PMID: 17283516     DOI: 10.1080/09548980600931995

Source DB:  PubMed          Journal:  Network        ISSN: 0954-898X            Impact factor:   1.273


  34 in total

1.  Role of homeostasis in learning sparse representations.

Authors:  Laurent U Perrinet
Journal:  Neural Comput       Date:  2010-07       Impact factor: 2.026

2.  The impact on midlevel vision of statistically optimal divisive normalization in V1.

Authors:  Ruben Coen-Cagli; Odelia Schwartz
Journal:  J Vis       Date:  2013-07-15       Impact factor: 2.240

3.  The spatiotemporal frequency tuning of LGN receptive field facilitates neural discrimination of natural stimuli.

Authors:  Zhongchao Tan; Haishan Yao
Journal:  J Neurosci       Date:  2009-09-09       Impact factor: 6.167

4.  Critical and maximally informative encoding between neural populations in the retina.

Authors:  David B Kastner; Stephen A Baccus; Tatyana O Sharpee
Journal:  Proc Natl Acad Sci U S A       Date:  2015-02-09       Impact factor: 11.205

5.  Nonlinear convergence boosts information coding in circuits with parallel outputs.

Authors:  Gabrielle J Gutierrez; Fred Rieke; Eric T Shea-Brown
Journal:  Proc Natl Acad Sci U S A       Date:  2021-02-23       Impact factor: 11.205

6.  Bottom-up attention: pulsed PCA transform and pulsed cosine transform.

Authors:  Ying Yu; Bin Wang; Liming Zhang
Journal:  Cogn Neurodyn       Date:  2011-05-18       Impact factor: 5.082

7.  Visual attention and flexible normalization pools.

Authors:  Odelia Schwartz; Ruben Coen-Cagli
Journal:  J Vis       Date:  2013-01-23       Impact factor: 2.240

8.  Statistics of the vestibular input experienced during natural self-motion: implications for neural processing.

Authors:  Jérome Carriot; Mohsen Jamali; Maurice J Chacron; Kathleen E Cullen
Journal:  J Neurosci       Date:  2014-06-11       Impact factor: 6.167

Review 9.  Early transformations in odor representation.

Authors:  Thomas A Cleland
Journal:  Trends Neurosci       Date:  2010-01-08       Impact factor: 13.837

10.  Natural image coding in V1: how much use is orientation selectivity?

Authors:  Jan Eichhorn; Fabian Sinz; Matthias Bethge
Journal:  PLoS Comput Biol       Date:  2009-04-03       Impact factor: 4.475

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