Literature DB >> 22238086

Neural correlation is stimulus modulated by feedforward inhibitory circuitry.

Jason W Middleton1, Cyrus Omar, Brent Doiron, Daniel J Simons.   

Abstract

Correlated variability of neural spiking activity has important consequences for signal processing. How incoming sensory signals shape correlations of population responses remains unclear. Cross-correlations between spiking of different neurons may be particularly consequential in sparsely firing neural populations such as those found in layer 2/3 of sensory cortex. In rat whisker barrel cortex, we found that pairs of excitatory layer 2/3 neurons exhibit similarly low levels of spike count correlation during both spontaneous and sensory-evoked states. The spontaneous activity of excitatory-inhibitory neuron pairs is positively correlated, while sensory stimuli actively decorrelate joint responses. Computational modeling shows how threshold nonlinearities and local inhibition form the basis of a general decorrelating mechanism. We show that inhibitory population activity maintains low correlations in excitatory populations, especially during periods of sensory-evoked coactivation. The role of feedforward inhibition has been previously described in the context of trial-averaged phenomena. Our findings reveal a novel role for inhibition to shape correlations of neural variability and thereby prevent excessive correlations in the face of feedforward sensory-evoked activation.

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Year:  2012        PMID: 22238086      PMCID: PMC3282531          DOI: 10.1523/JNEUROSCI.3474-11.2012

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  65 in total

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2.  Stimulus dependence of neuronal correlation in primary visual cortex of the macaque.

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8.  Multi-whisker stimulation and its effects on vibrissa units in rat SmI barrel cortex.

Authors:  D J Simons
Journal:  Brain Res       Date:  1983-10-03       Impact factor: 3.252

9.  Balanced excitation and inhibition determine spike timing during frequency adaptation.

Authors:  Michael J Higley; Diego Contreras
Journal:  J Neurosci       Date:  2006-01-11       Impact factor: 6.167

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Authors:  Anne-Marie M Oswald; Brent Doiron; John Rinzel; Alex D Reyes
Journal:  J Neurosci       Date:  2009-08-19       Impact factor: 6.167

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  41 in total

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2.  Synaptic input correlations leading to membrane potential decorrelation of spontaneous activity in cortex.

Authors:  Michael Graupner; Alex D Reyes
Journal:  J Neurosci       Date:  2013-09-18       Impact factor: 6.167

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4.  Adaptive shaping of cortical response selectivity in the vibrissa pathway.

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Journal:  Proc Natl Acad Sci U S A       Date:  2015-03-04       Impact factor: 11.205

6.  Frequency-separated principal component analysis of cortical population activity.

Authors:  Jean-Philippe Thivierge
Journal:  J Neurophysiol       Date:  2020-07-29       Impact factor: 2.714

7.  Adaptation modulates correlated subthreshold response variability in visual cortex.

Authors:  Nathaniel C Wright; Mahmood S Hoseini; Ralf Wessel
Journal:  J Neurophysiol       Date:  2017-06-07       Impact factor: 2.714

8.  Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations.

Authors:  Robert Rosenbaum; Jonathan E Rubin; Brent Doiron
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9.  Circuit mechanisms revealed by spike-timing correlations in macaque area MT.

Authors:  Xin Huang; Stephen G Lisberger
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Review 10.  Inhibitory Interneurons Regulate Temporal Precision and Correlations in Cortical Circuits.

Authors:  Jessica A Cardin
Journal:  Trends Neurosci       Date:  2018-09-25       Impact factor: 13.837

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