1 code implementation • NeurIPS 2023 • Chaofei Fan, Nick Hahn, Foram Kamdar, Donald Avansino, Guy H. Wilson, Leigh Hochberg, Krishna V. Shenoy, Jaimie M. Henderson, Francis R. Willett
Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.
2 code implementations • 9 Sep 2021 • Felix Pei, Joel Ye, David Zoltowski, Anqi Wu, Raeed H. Chowdhury, Hansem Sohn, Joseph E. O'Doherty, Krishna V. Shenoy, Matthew T. Kaufman, Mark Churchland, Mehrdad Jazayeri, Lee E. Miller, Jonathan Pillow, Il Memming Park, Eva L. Dyer, Chethan Pandarinath
We curate four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas.
no code implementations • NeurIPS 2020 • Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo
Here, we develop a novel learning rule designed to minimize interference between sequentially learned tasks in recurrent networks.
no code implementations • 19 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.
no code implementations • 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.
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.
no code implementations • NeurIPS 2011 • Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
Neurons in the neocortex code and compute as part of a locally interconnected population.
no code implementations • NeurIPS 2011 • Julie Dethier, Paul Nuyujukian, Chris Eliasmith, Terrence C. Stewart, Shauki A. Elasaad, Krishna V. Shenoy, Kwabena A. Boahen
The Kalman filter was trained to predict the arm’s velocity and mapped on to the SNN using the Neural Engineer- ing Framework (NEF).
no code implementations • NeurIPS 2008 • Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution.