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The Role of Internal Signals in Structuring V1 Population Activity

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Ecker,  A
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Tolias,  A
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Denfield, G., Ecker, A., & Tolias, A. (2017). The Role of Internal Signals in Structuring V1 Population Activity. Poster presented at 27th Annual Rush and Helen Record Neuroscience Forum, Galveston, TX, USA.


Cite as: http://hdl.handle.net/21.11116/0000-0000-C50F-2
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. We argue that this variability reflects, and is due to, computations internal to the brain. Relatively little research has examined the effect on neuronal responses of fluctuations in internal signals such as cortical state and attention, leaving a number of uncontrolled parameters that may contribute to neuronal variability. Attention increases neuronal response gain in a spatial and feature selective manner. We hypothesize that fluctuations in the strength and focus of attention 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. 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. We use multi-electrode recordings 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. Our results also indicate neuronal covariability is elevated in conditions in which attention fluctuates. 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.