Using perceptual decision-making to understand the role of selective inhibitory activity in cortical computation

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Cortical circuits perform computations to generate appropriate behaviors based upon diverse sensory inputs. These computations are central to an animal maintaining its health and long- term survival. An example of this type of computation are perceptual decision-making tasks where an animal must weigh sensory evidence to choose a behavior which will elicit a reward. The classical circuit models of decision-making focus solely on the effects of recurrent excitation, treating inhibitory neurons as agnostic facilitators of competition between excitatory subpopulations. However, this view of inhibitory neurons is at odds with experiment results which show a diversity of interneuron tuning and connectivity across the cortex, recently in the decision-making context. I propose to develop new models of cortical decision-making circuits which parameterizes selectivity of connections between subpopulations within the excitatory and inhibitory populations to understand how selectivity shapes the attractor dynamics underlying decision-making and how these dynamics represent animal choice. Based on the analysis of these models, I will establish an updated theoretical framework for the neural circuit mechanisms of decision-making behaviors which more fully account the intricacies of cortical circuit structure and more fully represent the diversity of neuronal cell-types. The role of inhibitory selectivity in facilitating task learning will be investigated using artificial neural networks as a proxy. Finally, single cell resolution calcium activity will be measured from a labeled inhibitory cell-type. This work will address how circuit structure and cell-type shape population dynamics underlying decision-making and how local cortical processes generate meaningful behaviors.