How do we know our social rank? Most social species, from insects to humans, self-organize into social dominance hierarchies (1-4). The establishment of social ranks serves to decrease aggression, conserve energy, and maximize survival for the entire group (5-8). Despite dominance behaviors being critical for successful interactions and ultimately, survival, we have only begun to learn how the brain represents social rank (9-12) and guides behavior based on this representation. The medial prefrontal cortex (mPFC) has been implicated in the expression of social dominance in rodents (10,11), and in social rank learning in humans (13,14). Yet precisely how the mPFC encodes rank and which circuits mediate this computation is not known. We developed a trial-based social competition assay in which mice compete for rewards, as well as a computer vision tool to track multiple, unmarked animals. With the development of a deep learning computer vision tool (AlphaTracker) and wireless electrophysiology recording devices, we have established a novel platform to facilitate quantitative examination of how the brain gives rise to social behaviors. We describe nine behavioral states during social competition that were accurately decoded from mPFC ensemble activity using a hidden Markov model combined with generalized linear models (HMM-GLM). Population dynamics in the mPFC were predictive of social rank and competitive success. This population-level rank representation translated into differences in the individual cell responses to task-relevant events across ranks. Finally, we demonstrate that mPFC cells that project to the lateral hypothalamus contribute to the prediction of social rank and promote dominance behavior during the reward competition. Thus, we reveal a cortico-hypothalamic circuit by which mPFC exerts topdown modulation of social dominance. Main TextThe medial prefrontal cortex (mPFC) is best known for its role in working memory, decision-making, reward learning and goal-oriented behavior [15][16][17][18][19] . Theories about mPFC function emphasize that it integrates sensory and limbic information to exibly guide behavior based on task rules 20,21 . mPFC circuitry has also been broadly implicated in social cognition [22][23][24] , social behaviors 25,26 , social

A functional interplay of bottom‐up and top‐down processing allows an individual to appropriately respond to the dynamic environment around them. These processing modalities can be represented as attractor states using a dynamical systems model of the brain. The transition probability to move from one attractor state to another is dependent on the stability, depth, neuromodulatory tone, and tonic changes in plasticity. However, how does the relationship between these states change in disease states, such as anxiety or depression? We describe bottom‐up and top‐down processing from Marr's computational‐algorithmic‐implementation perspective to understand depressive and anxious disease states. We illustrate examples of bottom‐up processing as basolateral amygdala signaling and projections and top‐down processing as medial prefrontal cortex internal signaling and projections. Understanding these internal processing dynamics can help us better model the multifaceted elements of anxiety and depression.

The activity of CA1 neurons in the rodent hippocampus represents multiple aspects of learning episodes, including cue and place information. Previous reports on cue and place representation in CA1 have examined activity in single neurons and population recordings during free exploration of an environment or when actions are directed to either cue or place aspects of memory tasks. To better understand cue and place memory representation in CA1, and how these interact during goal-directed navigation, we investigated population activity in CA1 during memory encoding and retrieval in a novel water task with two visibly distinct platforms, using mRNA for immediate early genes Arc and Homer1a as markers of neural activity. After training, relocating cues to new places induces an extensive, perhaps global, remapping of the memory code that is accompanied by altered navigation and rapid learning of new cue-place information. In addition, we have found a significant relationship between the extent of reactivation and overall cue choice accuracy. These findings demonstrate an important relationship between population remapping in CA1 and memory-guided behavior.

No abstract