Author

Nikhil Parthasarathy

Stanford University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Assay:imaging· 1 Organism:non-human primate· 1 Approach:simulation· 1

Frequent Co-Authors

E. J. ChichilniskyiD· 2 shared Brian Arie WandelliD· 1 shared Cordelia R. Erickson-DavisiD· 1 shared David H. BrainardiD· 1 shared Eleanor Batty· 1 shared Fred M RiekeiD· 1 shared James R. GoldeniD· 1 shared Liam PaninskiiD· 1 shared

Activity by Year

2018-2018 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersNeuroscience

E. J. ChichilniskyORCID

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2 papers

Recent·Most cited

Simulation of visual perception and learning with a retinal prosthesis

James R. Golden, Cordelia R. Erickson-Davis, Nicolas P. Cottaris, Nikhil Parthasarathy, Fred M Rieke, David H. Brainard, Brian Arie Wandell, E. J. Chichilnisky

This paper presents a computational framework to simulate visual perception with a retinal prosthesis and assesses how learning might improve inferred visual perception.

Journal of Neural Engineering · Nov 20, 2018Peer-reviewedNeuroscience

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

Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E. J. Chichilnisky, Liam Paninski

Linear decoding followed by a convolutional autoencoder reconstructs natural images from 5398 simulated macaque retinal ganglion cell responses better than linear decoding (test mean squared error 0.0051 vs 0.0077).

bioRxiv · May 28, 2018 · PreprintNeuroscience✦ In-depth digest