Mar 14, 2020

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

IEEE Transactions on Cybernetics
Qi YanJian 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 differe...Continue Reading

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Mentioned in this Paper

Acid-labile subunit, Drosophila
Gene Circuits
Biological Neural Networks
Neural Networks (Anatomic)
Reversal Learning
Neuron of Retina
Visual System
Filter - Medical Device

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