Abstract
SummaryCorrelated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states (“attractors”) or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic “stabilized supralinear network”), best explains these modulations. Given the supralinear input/output functions of cortical neurons, increased stimulus drive strengthens effective network connectivity. This shifts the balance from interactions that amplify variability to suppressive inhibitory feedback, quenching correlated variability around more strongly driven steady states. Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression. Specifying the cortical operating regime is key to understanding the computations underlying perception.
Highlights
Neuronal activity throughout cerebral cortex is variable, both temporally during epochs of stationary dynamics and across repeated trials despite constant stimulus or task conditions (Softky and Koch, 1993; Churchland et al, 2010)
In order to study response variability, we added to this model a stochastic component in the membrane potential dynamics of all cells
We found that ignoring the variability-increasing effects, which are characteristic of excitatory-inhibitory dynamics (Kriener et al, 2008; Murphy and Miller, 2009) and largely absent from models that do not include separate excitatory and inhibitory populations, can fail to capture the full extent of variability modulation and lead to an underestimation of the level of spontaneous variability obtained at zero-to-weak input levels (Figure S4)
Summary
Neuronal activity throughout cerebral cortex is variable, both temporally during epochs of stationary dynamics and across repeated trials despite constant stimulus or task conditions (Softky and Koch, 1993; Churchland et al, 2010). In area MT, variability is quenched more strongly in cells that respond best to the stimulus, and correlations decrease more among neurons with similar stimulus preferences (Ponce-Alvarez et al, 2013; Lombardo et al, 2015). These patterned modulations of variability are increasingly included in quantitative analyses of neural recordings (Renart and Machens, 2014; Orban et al, 2016), it is still unclear what they imply about the dynamical regime in which the cortex operates
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