Zheng, Y, Jia, S, Yu, Z et al. (2 more authors) (2021) Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks. Patterns, 2 (10). 100350. p. 100350. ISSN 2666-3899
Abstract
Traditional models of retinal system identification analyze the neural response to artificial stimuli using models consisting of predefined components. The model design is limited to prior knowledge, and the artificial stimuli are too simple to be compared with stimuli processed by the retina. To fill in this gap with an explainable model that reveals how a population of neurons work together to encode the larger field of natural scenes, here we used a deep-learning model for identifying the computational elements of the retinal circuit that contribute to learning the dynamics of natural scenes. Experimental results verify that the recurrent connection plays a key role in encoding complex dynamic visual scenes while learning biological computational underpinnings of the retinal circuit. In addition, the proposed models reveal both the shapes and the locations of the spatiotemporal receptive fields of ganglion cells.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2021 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | convolutional neural network; recurrent neural network; neural coding; visual coding; retina; video analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Oct 2021 12:36 |
Last Modified: | 12 Oct 2021 12:36 |
Status: | Published |
Publisher: | Cell Press |
Identification Number: | 10.1016/j.patter.2021.100350 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179040 |