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Elife. 2017 Jun 7;6. pii: e23978. doi: 10.7554/eLife.23978.

Attentional modulation of neuronal variability in circuit models of cortex.

Author information

1
Program for Neural Computation, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, United States.
2
Department of Mathematics, University of Pittsburgh, Pittsburgh, United States.
3
Center for the Neural Basis of Cognition, Pittsburgh, United States.
4
Allen Institute for Brain Science, Seattle, United States.
5
Department of Neuroscience, University of Pittsburgh, Pittsburgh, United States.

Abstract

The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition.

KEYWORDS:

inhibitory feedback; mean field model; neural correlates of attention; neuroscience; noise correlations; rhesus macaque

PMID:
28590902
PMCID:
PMC5476447
DOI:
10.7554/eLife.23978
[Indexed for MEDLINE]
Free PMC Article

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