Literature DB >> 16860742

How much the eye tells the brain.

Kristin Koch1, Judith McLean, Ronen Segev, Michael A Freed, Michael J Berry, Vijay Balasubramanian, Peter Sterling.   

Abstract

In the classic "What the frog's eye tells the frog's brain," Lettvin and colleagues showed that different types of retinal ganglion cell send specific kinds of information. For example, one type responds best to a dark, convex form moving centripetally (a fly). Here we consider a complementary question: how much information does the retina send and how is it apportioned among different cell types? Recording from guinea pig retina on a multi-electrode array and presenting various types of motion in natural scenes, we measured information rates for seven types of ganglion cell. Mean rates varied across cell types (6-13 bits . s(-1)) more than across stimuli. Sluggish cells transmitted information at lower rates than brisk cells, but because of trade-offs between noise and temporal correlation, all types had the same coding efficiency. Calculating the proportions of each cell type from receptive field size and coverage factor, we conclude (assuming independence) that the approximately 10(5) ganglion cells transmit on the order of 875,000 bits . s(-1). Because sluggish cells are equally efficient but more numerous, they account for most of the information. With approximately 10(6) ganglion cells, the human retina would transmit data at roughly the rate of an Ethernet connection.

Entities:  

Mesh:

Year:  2006        PMID: 16860742      PMCID: PMC1564115          DOI: 10.1016/j.cub.2006.05.056

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


  36 in total

1.  Metabolically efficient information processing.

Authors:  V Balasubramanian; D Kimber; M J Berry
Journal:  Neural Comput       Date:  2001-04       Impact factor: 2.026

2.  The metabolic cost of neural information.

Authors:  S B Laughlin; R R de Ruyter van Steveninck; J C Anderson
Journal:  Nat Neurosci       Date:  1998-05       Impact factor: 24.884

3.  Recording spikes from a large fraction of the ganglion cells in a retinal patch.

Authors:  Ronen Segev; Joe Goodhouse; Jason Puchalla; Michael J Berry
Journal:  Nat Neurosci       Date:  2004-10       Impact factor: 24.884

4.  Functional organization of ganglion cells in the salamander retina.

Authors:  Ronen Segev; Jason Puchalla; Michael J Berry
Journal:  J Neurophysiol       Date:  2005-11-23       Impact factor: 2.714

5.  The neurons of the retinal ganglion cell layer of the guinea pig: quantitative analysis of their distribution and size.

Authors:  J L Do-Nascimento; R S Do-Nascimento; B A Damasceno; L C Silveira
Journal:  Braz J Med Biol Res       Date:  1991       Impact factor: 2.590

6.  Mosaic arrangement of ganglion cell receptive fields in rabbit retina.

Authors:  S H Devries; D A Baylor
Journal:  J Neurophysiol       Date:  1997-10       Impact factor: 2.714

7.  The Psychophysics Toolbox.

Authors:  D H Brainard
Journal:  Spat Vis       Date:  1997

8.  Relations between the statistics of natural images and the response properties of cortical cells.

Authors:  D J Field
Journal:  J Opt Soc Am A       Date:  1987-12       Impact factor: 2.129

9.  Efficient coding of natural time varying images in the early visual system.

Authors:  M P Eckert; G Buchsbaum
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  1993-03-29       Impact factor: 6.237

10.  The distribution of the alpha type of ganglion cells in the cat's retina.

Authors:  H Wässle; W R Levick; B G Cleland
Journal:  J Comp Neurol       Date:  1975-02-01       Impact factor: 3.215

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  42 in total

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Authors:  Wilson S Geisler
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2.  Retina is structured to process an excess of darkness in natural scenes.

Authors:  Charles P Ratliff; Bart G Borghuis; Yen-Hong Kao; Peter Sterling; Vijay Balasubramanian
Journal:  Proc Natl Acad Sci U S A       Date:  2010-09-20       Impact factor: 11.205

3.  Spatial and temporal correlations of spike trains in frog retinal ganglion cells.

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4.  Design of a neuronal array.

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5.  Probability distributions of the logarithm of inter-spike intervals yield accurate entropy estimates from small datasets.

Authors:  Alan D Dorval
Journal:  J Neurosci Methods       Date:  2008-05-23       Impact factor: 2.390

Review 6.  Receptive fields and functional architecture in the retina.

Authors:  Vijay Balasubramanian; Peter Sterling
Journal:  J Physiol       Date:  2009-06-15       Impact factor: 5.182

7.  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

8.  Pitting binding against selection--electrophysiological measures of feature-based attention are attenuated by Gestalt object grouping.

Authors:  Adam C Snyder; Ian C Fiebelkorn; John J Foxe
Journal:  Eur J Neurosci       Date:  2012-03       Impact factor: 3.386

9.  Design of a trichromatic cone array.

Authors:  Patrick Garrigan; Charles P Ratliff; Jennifer M Klein; Peter Sterling; David H Brainard; Vijay Balasubramanian
Journal:  PLoS Comput Biol       Date:  2010-02-12       Impact factor: 4.475

10.  Coding efficiency of fly motion processing is set by firing rate, not firing precision.

Authors:  Deusdedit Lineu Spavieri; Hubert Eichner; Alexander Borst
Journal:  PLoS Comput Biol       Date:  2010-07-22       Impact factor: 4.475

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