Project Summary / Abstract Our strongest memories often stem from our most rewarding experiences, allowing us to learn what features of experience predict reward and to use these predictions in the future. The ability to remember rewarding spatial locations, such as food sources, is crucial for survival. In humans, reward memory can become dysfunctional in memory disorders and mental illnesses like drug addiction, highlighting the need to understand how the brain amplifies information associated with rewards. The hippocampus and medial entorhinal cortex (MEC) comprise a potential neural circuit for this amplification process. Neurons within these regions construct and update a neural map of spatial experience, notably "overrepresenting" reward locations within the neural activity. However, it remains unclear exactly what aspects of the rewarding experience the overrepresentation encodes and how this information is learned. The goal of this proposal is to understand the neural dynamics of how the reward overrepresentation develops in both the hippocampus and MEC and how this process is synchronized within the hippocampal-MEC circuit. To achieve this goal, the proposal combines powerful neural recordings technologies across rodent species, using innovative behavioral tasks that disentangle the reward itself from sensory stimuli, movement dynamics, and the cognitive demand of remembering a specific location which predicts reward. Aim 1 (K99) will use calcium imaging to determine whether changing cognitive demands shape the hippocampal reward overrepresentation over learning. Aim 2 (K99/R00) will use high-density electrophysiology to disentangle reward and cognitive demand in the MEC code, tightly controlling for motor correlates around goals. Aim 3 (R00, pilot K99) will combine simultaneous recordings in the hippocampus and MEC with inactivation of each region to dissect their reciprocal contributions to spatial reward memory and decision-making. This work will build on the candidate’s extensive expertise in electrophysiology and behavior by providing training in three key scientific domains, under lead mentor Lisa Giocomo: (1) computational modeling and statistical analysis with the guidance of co-mentor Scott Linderman, to illuminate how neural coding properties change in different task states; (2) calcium imaging with advisor Jun Ding, to solidify a toolkit to monitor the circuit dynamics underlying flexible coding; and (3) hippocampal and cortical population dynamics with advisors William Newsome and André Fenton, to understand how population activity is structured and coordinated across regions at moments of decision-making. The training plan will build professional skills in inclusive mentorship, lab management, and scientific communication to propel a transition to independence. Stanford University offers a collaborative, interdisciplinary, and supportive environment to pursue cutting-edge science and launch an independent career. The propose...