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Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Benjamin R. Cowley, Adam C. Snyder, Katerina Acar, Ryan C. Williamson, Byron M. Yu, Matthew A. Smith
doi: https://doi.org/10.1101/2020.01.10.902403
Benjamin R. Cowley
Princeton Neuroscience Institute, Princeton University, Princeton, NJDepartment of Machine Learning, Carnegie Mellon University, Pittsburgh, PACenter for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA
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Adam C. Snyder
Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PADepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PADepartment of Brain and Cognitive Sciences, University of Rochester, Rochester, NY
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Katerina Acar
Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PACarnegie Mellon Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PACenter for Neuroscience, University of Pittsburgh, Pittsburgh, PA
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Ryan C. Williamson
Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PACenter for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PAUniversity of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA
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Byron M. Yu
Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PACarnegie Mellon Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PADepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PADepartment of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
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Matthew A. Smith
Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PACarnegie Mellon Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PADepartment of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PADepartment of Ophthalmology, University of Pittsburgh, Pittsburgh, PA
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  • For correspondence: mattsmith@cmu.edu
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Abstract

An animal’s decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This internal state is a product of cognitive factors, such as fatigue, motivation, and arousal, but it is unclear how these factors influence the neural processes that encode the sensory stimulus and form a decision. We discovered that, over the timescale of tens of minutes during a perceptual decision-making task, animals slowly shifted their likelihood of reporting stimulus changes. They did this unprompted by task conditions. We recorded neural population activity from visual area V4 as well as prefrontal cortex, and found that the activity of both areas slowly drifted together with the behavioral fluctuations. We reasoned that such slow fluctuations in behavior could either be due to slow changes in how the sensory stimulus is processed or due to a process that acts independently of sensory processing. By analyzing the recorded activity in conjunction with models of perceptual decision-making, we found evidence for the slow drift in neural activity acting as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in the population activity across multiple brain areas, hidden from typical trial-averaged analyses and revealed only when considering the passage of time within each experimental session. Knowledge of this cognitive factor was critical in elucidating how sensory signals and the internal state together contribute to the decision-making process.

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Posted January 11, 2020.
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Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex
Benjamin R. Cowley, Adam C. Snyder, Katerina Acar, Ryan C. Williamson, Byron M. Yu, Matthew A. Smith
bioRxiv 2020.01.10.902403; doi: https://doi.org/10.1101/2020.01.10.902403
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Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex
Benjamin R. Cowley, Adam C. Snyder, Katerina Acar, Ryan C. Williamson, Byron M. Yu, Matthew A. Smith
bioRxiv 2020.01.10.902403; doi: https://doi.org/10.1101/2020.01.10.902403

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