Literature DB >> 17700699

Correlation between neural spike trains increases with firing rate.

Jaime de la Rocha1, Brent Doiron, Eric Shea-Brown, Kresimir Josić, Alex Reyes.   

Abstract

Populations of neurons in the retina, olfactory system, visual and somatosensory thalamus, and several cortical regions show temporal correlation between the discharge times of their action potentials (spike trains). Correlated firing has been linked to stimulus encoding, attention, stimulus discrimination, and motor behaviour. Nevertheless, the mechanisms underlying correlated spiking are poorly understood, and its coding implications are still debated. It is not clear, for instance, whether correlations between the discharges of two neurons are determined solely by the correlation between their afferent currents, or whether they also depend on the mean and variance of the input. We addressed this question by computing the spike train correlation coefficient of unconnected pairs of in vitro cortical neurons receiving correlated inputs. Notably, even when the input correlation remained fixed, the spike train output correlation increased with the firing rate, but was largely independent of spike train variability. With a combination of analytical techniques and numerical simulations using 'integrate-and-fire' neuron models we show that this relationship between output correlation and firing rate is robust to input heterogeneities. Finally, this overlooked relationship is replicated by a standard threshold-linear model, demonstrating the universality of the result. This connection between the rate and correlation of spiking activity links two fundamental features of the neural code.

Mesh:

Year:  2007        PMID: 17700699     DOI: 10.1038/nature06028

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  295 in total

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7.  Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity.

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8.  Spontaneous Fluctuations in Visual Cortical Responses Influence Population Coding Accuracy.

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Review 9.  From the statistics of connectivity to the statistics of spike times in neuronal networks.

Authors:  Gabriel Koch Ocker; Yu Hu; Michael A Buice; Brent Doiron; Krešimir Josić; Robert Rosenbaum; Eric Shea-Brown
Journal:  Curr Opin Neurobiol       Date:  2017-08-30       Impact factor: 6.627

10.  Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex.

Authors:  Joshua D Downer; Brittany Rapone; Jessica Verhein; Kevin N O'Connor; Mitchell L Sutter
Journal:  J Neurosci       Date:  2017-04-21       Impact factor: 6.167

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