Individual variability of neural computations in the primate retina

Nishal P Shah; Nora Brackbill; Ryan Samarakoon; Colleen Rhoades; Alexandra Kling; Alexander Sher; Alan Litke; Yoram Singer; Jonathon Shlens; E J Chichilnisky

doi:10.1016/j.neuron.2021.11.026

Individual variability of neural computations in the primate retina

Neuron. 2022 Feb 16;110(4):698-708.e5. doi: 10.1016/j.neuron.2021.11.026. Epub 2021 Dec 20.

Authors

Affiliations

¹ Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA. Electronic address: bhaishahster@gmail.com.
² Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Physics, Stanford University, Stanford, CA 94305, USA.
³ Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
⁴ Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
⁵ University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
⁶ WorldQuant, LLC, 1700 E Putnam Ave., Old Greenwich, CT 06870, USA.
⁷ Google Brain, 1600 Amphitheatre Pkwy., Mountain View, CA 94043, USA.

Abstract

Variation in the neural code contributes to making each individual unique. We probed neural code variation using ∼100 population recordings from major ganglion cell types in the macaque retina, combined with an interpretable computational representation of individual variability. This representation captured variation and covariation in properties such as nonlinearity, temporal dynamics, and spatial receptive field size and preserved invariances such as asymmetries between On and Off cells. The covariation of response properties in different cell types was associated with the proximity of lamination of their synaptic input. Surprisingly, male retinas exhibited higher firing rates and faster temporal integration than female retinas. Exploiting data from previously recorded retinas enabled efficient characterization of a new macaque retina, and of a human retina. Simulations indicated that combining a large dataset of retinal recordings with behavioral feedback could reveal the neural code in a living human and thus improve vision restoration with retinal implants.

Keywords: artificial intelligence; artificial retina; encoding models; individual variability; machine learning; neural prosthesis; response modeling; retina; sensory encoding; vision.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
Female
Macaca
Male
Photic Stimulation
Retina* / physiology
Retinal Ganglion Cells* / physiology
Vision, Ocular

Abstract

Publication types

MeSH terms

Grants and funding