Cortical ensembles orchestrate social competition through hypothalamic outputs.

Most social species self-organize into dominance hierarchies1,2, which decreases aggression and conserves energy3,4, but it is not clear how individuals know their social rank. We have only begun to learn how the brain represents social rank5-9 and guides behaviour on the basis of this representation. The medial prefrontal cortex (mPFC) is involved in social dominance in rodents7,8 and humans10,11. Yet, precisely how the mPFC encodes relative social rank and which circuits mediate this computation is not known. We developed a social competition assay in which mice compete for rewards, as well as a computer vision tool (AlphaTracker) to track multiple, unmarked animals. A hidden Markov model combined with generalized linear models was able to decode social competition behaviour from mPFC ensemble activity. Population dynamics in the mPFC predicted social rank and competitive success. Finally, we demonstrate that mPFC cells that project to the lateral hypothalamus promote dominance behaviour during reward competition. Thus, we reveal a cortico-hypothalamic circuit by which the mPFC exerts top-down modulation of social dominance.

Measuring and modeling the motor system with machine learning.

The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental work, and in this review we discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems. We also give our perspective on new avenues, where markerless motion capture combined with biomechanical modeling and neural networks could be a new platform for hypothesis-driven research.

Subjective feeling of re-experiencing past events using immersive virtual reality prevents a loss of episodic memory.

INTRODUCTION: Personally meaningful past episodes, defined as episodic memories (EM), are subjectively re-experienced from the natural perspective and location of one's own body, as described by bodily self-consciousness (BSC). Neurobiological mechanisms of memory consolidation suggest how initially irrelevant episodes may be remembered, if related information makes them gain importance later in time, leading for instance, to a retroactive memory strengthening in humans. METHODS: Using an immersive virtual reality system, we were able to directly manipulate the presence or absence of one's body, which seems to prevent a loss of initially irrelevant, self-unrelated past events. RESULTS AND CONCLUSION: Our findings provide an evidence that personally meaningful memories of our past are not fixed, but may be strengthened by later events, and that body-related integration is important for the successful recall of episodic memories.