Date of Graduation

5-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Physics (PhD)

Degree Level

Graduate

Department

Physics

Advisor

Woodrow Shew

Committee Member

Jiali Li

Second Committee Member

Nathan Parks

Third Committee Member

Pradeep Kumar

Abstract

Cerebral cortex exhibits vigorous ongoing, internal neural activity even with no sensory input is present or the animal is minimally engaged in a task or behavior. This internal ongoing activity is not static; the ‘cortical state’ varies ranging from synchronous and highly correlated activity to asynchronous and weakly correlated neural activity. The main goal of the work presented here is to understand how changes in cortical states effect several aspects of cortical function and dynamics.

To meet this goal, we did three separate projects. First, we compared the predictability of neuronal network dynamics across cortical states in somatosensory cortex of anesthetized rats. We found that predictably was not static; it depends on cortical state and the duration of prediction. Second, we implemented a closed-loop feedback control to control the neural activity in the motor cortex of anesthetized mice. We found a trade-off between the accuracy and cost of control as we tuned the cortical state with anesthetic drugs. Finally, we studied how single neuron is related to summed activity of large population, referred to as population coupling. We found different neurons within the same network can have diverse population coupling and that this coupling change if we manipulate inhibitory signaling in the network.

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