Applied statistical and machine learning methods (PCA, Procrustes alignment, clustering etc.) on place cell data to study how different experiences are encoded in the hippocampal ensemble across subjects.
Collected and analyzed data from simultaneous in vivo electrophysiology recording in the hippocampus and fiber photometry recording in the ventral striatum.
National Tsing Hua University, Hsinchu, Taiwan 2015
B.A., Economics
Professional Experience
Center for Computational Neuroscience, Simons Foundation, New York, NY summer 2022
NeuroStats Lab (PI: Alex Williams) Summer Research Associate
Investigated conditions required to derive unique transformations between neural representations using Procrustes analysis.
New York University Shanghai, Shanghai, China 2016 - 2018
Erlich Lab (PI: Jeffery Erlich) Research Assistant
Designed a sequential decision-making task for rodents and developed computational modeling of (hierarchical) reinforcement learning to fit behavioral data.
Developed responsive frontend features like dashboard, resume viewer, interview scheduler, performance summary and corresponding backend API service.
Publications
Li, L., Ma, C., Li, J., Chen Y., Chen, H.-T., Erlich, J.C. (submitted). Encoding of spatial location by frontal orienting field neurons.
Chen, H.-T., Manning, J. R., & van der Meer, M. A. A. (2021). Between-subject prediction reveals a shared representational geometry in the rodent hippocampus. Current Biology: CB, 31(19), 4293–4304.e5.
Talks
Between-subject prediction reveals a shared representational geometry in the rodent hippocampus. 2022
Chen HT, Manning JR, van der Meer MA (2019) Shared representational geometry as an explanation for cross-subject prediction of place cell data from the rodent hippocampus. Society for Neuroscience, Chicago, IL.
Chen HT, Manning JR, van der Meer MA (2019) Between-subject prediction reveals a shared representational geometry in the rodent hippocampus. Computational and Systems Neuroscience (CoSyNe), Lisbon, Portugal.
*Rated in top 6% of abstracts over 1000 submissions
Technical Skills
Machine Learning and Data analysis: Numpy, Sklearn, Pytorch, Matplotlib
Programming Languages: Python, Matlab, R, Javascript, SQL, HTML, CSS
Web Development: React, Redux, NodeJS, ExpressJS, GraphQL
Data collection and Signal processing: In vivo electrophysiology, Fiber photometry
Honors and Awards
Neukom Prize for Outstanding Graduate Research in Computational Science 2021
Dartmouth GSC Student Professional Development Support Fund 2019
Dartmouth PBS Graduate Travel Award 2019
Uber Beijing Hachthon First Place 2016
NTHU Academic Achievement Award 2011
Teaching Experience
Neural Systems and Behavior course (TA for mouse module), Marine Biological Laboratories. summer 2021
Learning (TA), Dartmouth College spring 2021
Systems Neuroscience (Lab instructor), Dartmouth College 2019 - 2021