Optical measurement of causal functional connectivity in posterior parietal cortex

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Project Summary The mouse posterior parietal cortex (PPC) has emerged as an essential region for decision-making during memory-guided decision-making tasks. Neurons in the PPC typically respond selectively at a single point during a unique behavioral trial type; activity at the population level can be thought of as a choice-specific trajectory through state space. Many neurons respond selectively for behavioral choice; these responses resemble those observed in models incorporating winner-take-all dynamics, but the extent to which PPC functions as a winner- take-all network remains unknown. Key features of winner-take-all neural networks include choice-specific excitatory connectivity and mutual inhibition between pools of neurons with different choice preferences. To test models of neural circuit architecture in decision-making areas, we propose to develop a high-speed, high- throughput two-photon photostimulation approach to optically measure causal functional connectivity between neurons. We will first develop and characterize this photostimulation approach with in vivo cell-attached recordings. Next, we will apply this photostimulation approach to measure causal functional connectivity between excitatory neurons in PPC to test the degree of functional specificity in excitatory networks. These experiments will relate separately measured activity-behavior relationships to causal functional connectivity measurements between excitatory neurons and test whether neurons exhibiting similar choice preferences preferentially excite one another. Finally, we will measure the effect of stimulating GABAergic neurons in PPC while the mouse performs a memory-guided two-alternative forced choice task to measure causal functional connectivity between GABAergic neurons and excitatory neurons. If PPC exhibits winner-take-all dynamics, we would expect to find that GABAergic neurons preferentially inhibit excitatory neurons with opposite choice preferences. As a whole, we predict that this technique will generate powerful data that provide new insight into the microcircuitry of decision-making in PPC.