Attentional modulation of neuronal variability in circuit models of cortex

• Main Authors: Tatjana Kanashiro, Gabriel Koch Ocker, Marlene R Cohen, Brent Doiron
• Format: Article
• Language: English
• Published: eLife Sciences Publications Ltd 2017-06-01
• Series: eLife
• Subjects: noise correlations; inhibitory feedback; neural correlates of attention; mean field model
• Online Access: https://elifesciences.org/articles/23978

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.