1 code implementation • NeurIPS 2019 • Nishal Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan Litke, Liam Paninski, E.J. Chichilnisky
Large-scale, high-density electrical recording and stimulation in primate retina were used as a lab prototype for an artificial retina.
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.
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.
no code implementations • NeurIPS 2015 • Emile Richard, Georges A. Goetz, E.J. Chichilnisky
Here, we develop automated classifiers for functional identification of retinal ganglion cells, the output neurons of the retina, based solely on recorded voltage patterns on a large scale array.
no code implementations • NeurIPS 2014 • Kenneth W. Latimer, E.J. Chichilnisky, Fred Rieke, Jonathan W. Pillow
We show that the model fit to extracellular spike trains can predict excitatory and inhibitory conductances elicited by novel stimuli with nearly the same accuracy as a model trained directly with intracellular conductances.
no code implementations • NeurIPS 2001 • Odelia Schwartz, E.J. Chichilnisky, Eero P. Simoncelli
Spike-triggered averaging techniques are effective for linear characterization of neural responses.