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Revealing Fine Structures of the Retinal Receptive Field by Deep-Learning Networks

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journal contribution
posted on 2020-04-02, 11:59 authored by Qi Yan, Yajing Zheng, Shanshan Jia, Yichen Zhang, Zhaofei Yu, Feng Chen, Yonghong Tian, Tiejun Huang, Jian K Liu
Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the visual system in neuroscience. However, it is still not clear what is learned by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to the higher visual cortex. Here, we address this issue by focusing on single retinal ganglion cells with biophysical models and recording data from animals. By training CNNs with white noise images to predict neuronal responses, we found that fine structures of the retinal receptive field can be revealed. Specifically, convolutional filters learned are resembling biological components of the retinal circuit. This suggests that a CNN learning from one single retinal cell reveals a minimal neural network carried out in this cell. Furthermore, when CNNs learned from different cells are transferred between cells, there is a diversity of transfer learning performance, which indicates that CNNs are cell specific. Moreover, when CNNs are transferred between different types of input images, here white noise versus natural images, transfer learning shows a good performance, which implies that CNNs indeed capture the full computational ability of a single retinal cell for different inputs. Taken together, these results suggest that CNNs could be used to reveal structure components of neuronal circuits, and provide a powerful model for neural system identification.

Funding

10.13039/501100001809-National Natural Science Foundation of China; 10.13039/501100012152-National Postdoctoral Program for Innovative Talents; 10.13039/501100002858-China Postdoctoral Science Foundation; Tsinghua-Guoqiang Research Program; Zhejiang Lab; Royal Society Newton Advanced Fellowship

History

Citation

IEEE Transactions on Cybernetics, 2020

Author affiliation

Department of Neuroscience, Psychology and Behaviour

Version

  • AM (Accepted Manuscript)

Published in

IEEE Transactions on Cybernetics

Pagination

1 - 12

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

2168-2267

eissn

2168-2275

Copyright date

2020

Publisher version

https://ieeexplore.ieee.org/document/9031699

Language

en