Neuromatch Academy: Teaching Computational Neuroscience with global accessibility

no code implementations • 15 Dec 2020 • Tara van Viegen, Athena Akrami, Kate Bonnen, Eric DeWitt, Alexandre Hyafil, Helena Ledmyr, Grace W. Lindsay, Patrick Mineault, John D. Murray, Xaq Pitkow, Aina Puce, Madineh Sedigh-Sarvestani, Carsen Stringer, Titipat Achakulvisut, Elnaz Alikarami, Melvin Selim Atay, Eleanor Batty, Jeffrey C. Erlich, Byron V. Galbraith, Yueqi Guo, Ashley L. Juavinett, Matthew R. Krause, Songting Li, Marius Pachitariu, Elizabeth Straley, Davide Valeriani, Emma Vaughan, Maryam Vaziri-Pashkam, Michael L. Waskom, Gunnar Blohm, Konrad Kording, Paul Schrater, Brad Wyble, Sean Escola, Megan A. K. Peters

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects.

Management

BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos

1 code implementation • NeurIPS 2019 • Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott Linderman, Liam Paninski

Here we introduce a probabilistic framework for the analysis of behavioral video and neural activity.

YASS: Yet Another Spike Sorter

1 code implementation • NeurIPS 2017 • Jin Hyung Lee, David E. Carlson, Hooshmand Shokri Razaghi, Weichi Yao, Georges A. Goetz, Espen Hagen, Eleanor Batty, E.J. Chichilnisky, Gaute T. Einevoll, Liam Paninski

Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data.

Clustering Spike Sorting +1

Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons

1 code implementation • NeurIPS 2017 • Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E.J. Chichilnisky, Liam Paninski

Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces.

Bayesian Inference