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PI: Dyer, Eva 

Email: evadyer@gatech.edu

Institution: Georgia Tech

Title: A comparative framework for modeling the low-dimensional geometry of neural population states   

Grant #: EB029852  

Status: Active

Deliverables:

• C-H. Lin, M. Azabou, E.L. Dyer, Making transport more robust and interpretable by moving data through a small number of anchor points, to appear at the International Conference on Machine Learning (ICML), 2021 (Preprint, Code)
• M. Azabou, M. Gheshlaghi Azar, R. Liu, C-H. Lin, E.C. Johnson, K. Bhaskharan-Nair, M. Dabagia, K.B. Hengen, W. Gray-Roncal, M. Valko, E.L. Dyer, Mine Your Own vieW: Self-supervised learning through across-sample prediction, Feb 2021 (Preprint, Code)

2021 Brain PI Meeting

Update: 

Link to Poster: 

• M. Azabou, M. Gheshlaghi Azar, R. Liu, C-H. Lin, E.C. Johnson, K. Bhaskharan-Nair, M. Dabagia, K.B. Hengen, W. Gray-Roncal, M. Valko, E.L. Dyer, Building invariances into neural decoding through adaptive self-alignment (Poster #4026).    [Link to Poster]  [Code and Demos] 

Demo: [no demo]

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