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.


September 2019
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.

August 2019
Undergraduates Alicia Mercado (Mount Saint Mary's University) and Michelle Lam (UCLA) won the Best Poster Award at UCLA's Summer Undergraduate Research Program.

June 2019
Jonathan was named a UCLA Hellman Fellow for his work in building next generation brain-machine interfaces.

April 2019
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.

March 2018
PhD student Michael Kleinman was awarded a Canadian NSERC PGS-D Fellowship for his work on using RNNs to model neural computation.

September 2016
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.

August 2015
A novel decode algorithm incorporating neural population dynamics was published in Nature Communications, was featured in Stanford News