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Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

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journal contribution
posted on 20.05.2020, 12:12 by Z Yu, JK Liu, S Jia, Y Zhang, Y Zheng, Y Tian, T Huang
A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion, while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body. Incoming visual information can be processed by the brain in millisecond intervals. The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation. Thus, the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike. Closed-loop computation in a neuroprosthesis includes two stages: encoding a stimulus as a neuronal signal, and decoding it back into a stimulus. In this paper, we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos. We hypothesize that in order to obtain a better understanding of the computational principles in the retina, a hypercircuit view of the retina is necessary, in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina. The different building blocks of the retina, which include a diversity of cell types and synaptic connections—both chemical synapses and electrical synapses (gap junctions)—make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes. An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.

Funding

This work is supported by the National Basic Research Program of China (2015CB351806); the National Natural Science Foundation of China (61806011, 61825101, 61425025, and U1611461); the National Postdoctoral Program for Innovative Talents (BX20180005); the China Postdoctoral Science Foundation (2018M630036); the International Talent Exchange Program of Beijing Municipal Commission of Science and Technology (Z181100001018026); the Zhejiang Lab (2019KC0AB03 and 2019KC0AD02); and the Royal Society Newton Advanced Fellowship (NAF-R1-191082).

History

Citation

Engineering (2020)

Version

VoR (Version of Record)

Published in

Engineering

Publisher

Elsevier BV

issn

2095-8099

Acceptance date

10/06/2019

Copyright date

2020

Available date

21/02/2020

Language

en