Literature DB >> 9144251

The structure and precision of retinal spike trains.

M J Berry1, D K Warland, M Meister.   

Abstract

Assessing the reliability of neuronal spike trains is fundamental to an understanding of the neural code. We measured the reproducibility of retinal responses to repeated visual stimuli. In both tiger salamander and rabbit, the retinal ganglion cells responded to random flicker with discrete, brief periods of firing. For any given cell, these firing events covered only a small fraction of the total stimulus time, often less than 5%. Firing events were very reproducible from trial to trial: the timing jitter of individual spikes was as low as 1 msec, and the standard deviation in spike count was often less than 0.5 spikes. Comparing the precision of spike timing to that of the spike count showed that the timing of a firing event conveyed several times more visual information than its spike count. This sparseness and precision were general characteristics of ganglion cell responses, maintained over the broad ensemble of stimulus waveforms produced by random flicker, and over a range of contrasts. Thus, the responses of retinal ganglion cells are not properly described by a firing probability that varies continuously with the stimulus. Instead, these neurons elicit discrete firing events that may be the fundamental coding symbols in retinal spike trains.

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Year:  1997        PMID: 9144251      PMCID: PMC24692          DOI: 10.1073/pnas.94.10.5411

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  41 in total

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Authors:  A Arieli; A Sterkin; A Grinvald; A Aertsen
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Journal:  Vis Neurosci       Date:  1994 Jan-Feb       Impact factor: 3.241

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  150 in total

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7.  Decorrelation and efficient coding by retinal ganglion cells.

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8.  Information transmission rates of cat retinal ganglion cells.

Authors:  Christopher L Passaglia; John B Troy
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9.  A unified mechanism for spontaneous-rate and first-spike timing in the auditory nerve.

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10.  Deep Learning Models of the Retinal Response to Natural Scenes.

Authors:  Lane T McIntosh; Niru Maheswaranathan; Aran Nayebi; Surya Ganguli; Stephen A Baccus
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