A central issue facing systems neuroscience is defining the rich naturalistic behavioral repertoire that mice engage in under psychiatrically relevant situations. Recent advances in deep learning (e.g., DeepLabCut) have made frame by frame detailed pose estimation possible. However, this dense behavioral data requires new techniques for defining the ethogram (full description of behavior). To date, researchers have used frequency based time series approaches to tackle this problem, with significant limitations. An alternative approach would be to take advantage of new topology methods (persistent homology and directed algebraic topology) to characterize the shapes formed by mouse limb trajectories. Such an approach would have broad application in systems neuroscience. For this project, the student will use machine learning to label animal body parts, then topology to characterize the ethogram and compare the results to existing approaches.

One selected candidate will receive a stipend via the DSI Scholars program. Amount is subject to available funding.

Faculty Advisor

  • Professor: Alexander Harris
  • Department/School: Psychiatry/Medical Colllege
  • Location: Kolb Annex 137
  • We use systems neuroscience techniques including in vivo electrophysiology and optogenetics in awake behaving mice to understand the circuits underlying psychiatric symptoms.

Project Timeline

  • Earliest starting date: 3/1/2020

Candidate requirements

  • Skill sets: familiarity with directed algebraic topology and/or persistent homology
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible