Cortical Dynamics and Neural/Behavioral Performance

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What are the cellular and network cortical mechanisms of optimal neural and behavioral performance? Through what mechanisms do spontaneous and evoked changes in the waking state of the cortex influence sensory processing and behavior? The goal of my laboratory is to answer these broad and important questions at levels extending from cellular and synaptic properties, to local circuits, to thalamocortical networks, modulation, and behavior. Answering these questions are fundamental not only to understanding the normal operation of the brain, but also its operation in a variety of disorders, from schizophrenia, to ADHD, autism, and others. Our proposal is ambitious, but not unreasonable or unfocused. We have already made significant progress towards a broad outline of important pieces of the puzzle, and therefore are confident that we will make very significant progress towards a detailed clarification of these two fundamental questions during the funding period. More than a hundred years ago, two investigators, Yerkes and Dodson, noted that optimal performance on difficult detection tasks was related to arousal level in an “inverted-U” shaped fashion. Increases from low arousal to intermediate arousal would enhance performance on difficult tasks, while further increases in arousal from intermediate to high would decrease performance. This result suggests that there is an “optimal state” for both the brain and behavior. Surprisingly, until our recent study in behaving mice performing a difficult auditory detection task, the cortical activity or circuit representation of optimal state had not been investigated. Our investigation revealed that the optimal state for performance of a difficult auditory sensory detection task occurred at intermediate levels of arousal and was associated with the suppression of slow corticocortical and thalamocortical activity, a hyperpolarized and low variability of pyramidal cell membrane potential, and large amplitude and highly reliable evoked auditory cortical synaptic responses. In an indication of the broad nature of these effects, we observed that we could predict more than half of the variance in cortical neuronal membrane potential, action potential, and even behavioral performance simply by measuring the pupil diameter – an easily obtained measure of rapid (second to second) fluctuations in behavioral state. By explaining a large fraction of neuronal and behavioral variance, we have demonstrated that the brain is much more precise and reliable than previously thought. Here we propose to reveal the detailed cellular, modulatory, and network mechanisms that account for these prominent effects of state variation on neural and behavioral performance. Through a combination of state-of-the-art imaging, whole cell recording, optogenetic manipulation, and high quality behavioral monitoring, we will be able to detail the contribution of multiple neuronal and neuromodulatory pathways to the determination of optimal state for neural/behavioral responses.