Cracking Genetically Defined Neocortical Circuits across Learning and Behavior

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PROJECT SUMMARY How does our genome instruct the circuit-level neural computations that give rise to cognition? This is a fundamental question that aims to explain our cognitive capacity as humans. It requires a deep understanding for how gene expression in individual neurons relates to activity patterns across the population during behavior. Comprehensively integrating molecular, anatomical, functional, and behavioral measurements is the main technical challenge in achieving such an understanding. Here, I propose to determine how circuit-level implementations of gene expression define specific neural computations and learning rules in the neocortex. In the course of my work I will seek to address the following questions: 1) How pervasive are genetically defined circuit motifs in the neocortex and what do they compute? 2) How are activity-regulated genes induced and expressed across neocortical circuits during learning? Addressing this requires innovative approaches to characterize circuit components and their functional interactions. To this end, I will combine large-scale single-cell functional imaging and transcriptional profiling into an integrated methodological platform called CRACK (Comprehensive Readout of neuronal Activity and Cell type marKers). I will use this platform to “crack” genetically defined neural circuits underlying sensorimotor integration, higher-level sensory processing, and decision making in the mouse whisker sensorimotor system. I will first apply CRACK in a forward screen to identify novel circuit motifs by characterizing unique functional relationships between known cell types and validating their connections with subsequent anatomical measures. I will then apply this platform to survey the relationship for how learning drives plasticity in cortical circuits by generating intersectional circuit maps of activity patterns and immediate early gene expression in defined cell types. Through these projects, I aim to accelerate the discovery of core circuits underlying learning and behavior in the mammalian neocortex.