Our research group studies questions at the intersection of neuroscience and computation.
In particular, we develop and apply statistical signal processing and machine learning techniques to elucidate how populations of neurons carry out computations in the brain.
Further, we also develop experimental and algorithmic techniques for neural engineering applications, including brain-machine interfaces.
Michael Kleinman received a Travel Award for the 2019 Conference on Cognitive Computational Neuroscience. Check out his conference paper here
and our recent bioRxiv preprint
building on this work.
Undergraduates Alicia Mercado (Mount Saint Mary's University) and Michelle Lam (UCLA) won the Best Poster Award at UCLA's Summer Undergraduate Research Program.
Jonathan was named a UCLA Hellman Fellow for his work in building next generation brain-machine interfaces.
PhD student Peter Schuette was awarded a NSF Graduate Research Fellowship for his work in studying the representation of threat in rodents.
Peter was also awarded a UCLA Affiliates Scholarship.
PhD student Michael Kleinman was awarded a Canadian NSERC PGS-D Fellowship for his work on using RNNs to model neural computation.
We published a BMI used for typing in Proceedings of the IEEE
; the algorithm for this study was recently reported in IEEE TBME
This work was featured in The Wall Street Journal
, Stanford News
, IEEE Spectrum
, NPR KQED - Future of You
, The Verge
, and Wired UK
. Paul and I also did a Reddit AMA
regarding this paper.
A novel decode algorithm incorporating neural population dynamics was published in Nature Communications
, was featured in Stanford News