When sensory inputs are ambiguous, the brain builds an internal model to infer which events in the world caused this pattern of sensory activity. This process, called causal inference, provides a unifying framework for understanding how neural signals that represent beliefs about the structure of the world interact with incoming sensory signals to drive perception-action loops. This proposal focuses on perceptual interactions among object motion, object depth, and an animal's self-motion through the world, as a particular moving pattern of neural activity on the retina can be generated by many combinations of object motion in the world and self- motion. The overall hypothesis is that parietal and prefrontal neurons infer whether an object moves in the world, and that these signals flow through feedback projections to update task-relevant representations in extrastriate visual cortex. The goal of this project is to study causal inference in dynamic tasks, in which an animal's internal model of the world changes continuously. In a virtual reality navigation task in monkeys and mice, these experiments will explore brain computation and multi-area interactions in the naturalistic setting of continuous action and active sensing, as well as dynamic on-line inference about latent, task-relevant variables related to the internal model. This project will develop a causal inference version of a dynamic navigation task already in use in the Angelaki laboratory and then use population recordings and causal neural manipulations to test and refine the dynamic model developed by the theory team in Project A. The continuous-time latent variables of this model will be fitted to monkey and mouse behavioral data to reveal each animal's beliefs about the state of the world and interacting task-relevant variables, and to generate novel hypotheses about the neural dynamics. Using multi-electrode recordings and chemical and optogenetic manipulations, this project will test these hypotheses in four mutually interconnected monkey brain areas involved in visual perception, navigation, memory, and decision-making: parietal area 7a, prefrontal area 8aV, and extrastriate visual cortical areas MSTd (dorsal medial superior temporal) and MT (middle temporal). Finally, neural activity will be mapped throughout the mouse brain, with an emphasis on subcortical structures, using parallel recordings with Neuropixels probes for hypothesis-free identification of other areas that are modulated by this dynamic task, which will also serve to generalize the findings across species. Based on these findings, additional macaque brain regions will be targeted for recording and manipulation experiments as needed. Collectively, these experiments will rigorously test the computational framework of dynamic causal inference across species and brain areas. When compared with the complementary findings from trial-based tasks in Project B, successful completion of these experiments is expected to uncover general principles of the function of causal inference processes and top-down feedback connections during naturalistic and dynamically fluid behavior.
