Decision-making requires populations of neurons in the brain to collectively process sensory evidence and select appropriate behavioral responses. Neural population dynamics (NPDs), which describe how the responses of a population of individual neurons unfold over time, can provide an insightful view into these decision processes. Much is known about how individual neurons respond in select decision making tasks. However, little is known about how populations of neurons dynamically perform decision computations, and how resulting NPDs within a brain area are structured across many decision-making tasks. Shared features in NPDs across many tasks could indicate unifying neural mechanisms of computation that underlie the multi-functionality of a given neural circuit. This proposal aims to uncover the details and structure of these decision-related NPDs in human and nonhuman primate dorsal premotor cortex, an area tightly linked to both the function and dysfunction of decision making. Particular attention will be devoted to examining how NPDs are organized across multiple decision-making tasks and how those NPDs emerge during learning of new tasks. During the K99 phase, novel analytical tools will be developed for extracting NPDs from simultaneously recorded neural population activity and, importantly, for providing interpretable links between NPDs and their role in decision-related neural computation. With the goal of identifying unifying principles of decision-related computation, large-scale analyses will then integrate existing and newly collected neurophysiological datasets involving multiple decision-making tasks performed by humans and nonhuman primates. During the R00 phase, the proposed research will pivot toward understanding how NPDs emerge during learning to make new types of decisions. The proposal postulates that the ease, speed, and efficacy of learning all hinge on the extent by which critical neural circuits can leverage pre-existing neural mechanisms of computation. These concepts will be tested in collaborative human and nonhuman primate neu- rophysiological experiments, with guidance provided by interrogations of artificial neural networks posed with similar decision-related learning tasks. Upon completion, the proposed research will provide new fundamental knowledge concerning i) the multi-functional and adaptive role of premotor cortex across many decision-making tasks and ii) unifying principles of neural computation that support this flexibility. Better understanding decision- related circuits and their neural mechanisms could ultimately elucidate the basis for the numerous psychiatric disorders that impair decision making, which could eventually lead to improved diagnosis and treatment of these debilitating conditions.
/ Public Health Relevance Our brain?s ability to generate decisions can be severely disrupted by psychiatric disorders, such as anxiety, depression, bipolar disorder, and obsessive compulsive disorder. This proposal develops novel analytical and experimental approaches for understanding decision making at the level of populations of individual neurons in human and non-human primate premotor cortex, an area tightly linked to decision making. By integrating a large collection of new and existing datasets reflecting the involvement of premotor cortex in a wide range of decision- making tasks, the proposed research is expected to generate fundamental knowledge about unifying principles of decision-related neural computation, which may ultimately lead to improvements in the diagnosis and treat- ment of decision-impacting psychiatric conditions.
