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Abstract

Neuronal responses to repeated presentations of identical visual stimuli are variable. The source of this variability is unknown, but it is commonly treated as noise and seen as an obstacle to understanding neuronal activity. We argue that this variability is not noise but reflects, and is due to, computations internal to the brain. Internal signals such as cortical state or attention interact with sensory information processing in early sensory areas. However, little research has examined the effect of fluctuations in these signals on neuronal responses, leaving a number of uncontrolled parameters that may contribute to neuronal variability. One such variable is attention, which increases neuronal response gain in a spatial and feature selective manner. Both the strength of this modulation and the focus of attention are likely to vary from trial to trial, and we hypothesize that these fluctuations are a major source of neuronal response variability and covariability. We first examine a simple model of a gain-modulating signal acting on a population of neurons and show that fluctuations in attention can increase individual and shared variability and generate a variety of correlation structures relevant to population coding, including limited range and differential correlations. To test our model’s predictions experimentally, we devised a cued-spatial attention, change-detection task to induce varying degrees of fluctuation in the subject’s attentional signal by changing whether the subject must attend to one stimulus location while ignoring another, or attempt to attend to multiple locations simultaneously. We use multi-electrode recordings with laminar probes in primary visual cortex of macaques performing this task. We demonstrate that attention gain-modulates responses of V1 neurons in a manner consistent with results from higher-order areas. Consistent with our model’s predictions, our preliminary results indicate neuronal covariability is elevated in conditions in which attention fluctuates and that neurons are nearly independent when attention is focused. Overall, our results suggest that attentional fluctuations are an important contributor to neuronal variability and open the door to the use of statistical methods for inferring the state of these signals on behaviorally relevant timescales.