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.
Highlights
The behavioral state of the brain exerts a powerful influence on the cortical responses
Exploration of the neuronal mechanisms that underly such state changes has primarily centered around how various neuromodulators shift the cellular and synaptic properties of cortical circuits (Hasselmo, 1995; Lee and Dan, 2012; Noudoost and Moore, 2011; Moore and Zirnsak, 2017) a coherent theory linking the modulation of cortical circuits to an active desynchronization of population activity is lacking
Using population recordings from visual area V4 we identified rank one structure in the mapping of population spike count covariability between unattended and attended states
Summary
The behavioral state of the brain exerts a powerful influence on the cortical responses. Electrophysiological recordings from both rodents and primates show that the level of wakefulness (Steriade et al, 1993), active sensory exploration (Crochet et al, 2011), and attentional focus (Treue, 2001; Reynolds and Chelazzi, 2004; Gilbert and Sigman, 2007; Moore and Zirnsak, 2017) all modulate synaptic and spiking activity. Attention increases the firing rates of neurons engaged in feature- and spatial-based processing tasks (McAdams and Maunsell, 2000; Reynolds et al, 1999).
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