Search Results for author: Krishna V. Shenoy

Found 8 papers, 2 papers with code

Making brain-machine interfaces robust to future neural variability

no code implementations19 Oct 2016 David Sussillo, Sergey D. Stavisky, Jonathan C. Kao, Stephen I. Ryu, Krishna V. Shenoy

A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change.

Translation

High-dimensional neural spike train analysis with generalized count linear dynamical systems

1 code implementation NeurIPS 2015 Yuanjun Gao, Lars Busing, Krishna V. Shenoy, John P. Cunningham

Latent factor models have been widely used to analyze simultaneous recordings of spike trains from large, heterogeneous neural populations.

Variational Inference

Dynamical segmentation of single trials from population neural data

no code implementations NeurIPS 2011 Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani

Simultaneous recordings of many neurons embedded within a recurrently-connected cortical network may provide concurrent views into the dynamical processes of that network, and thus its computational function.

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