Revealing structure components of the retina by deep learning networks

Abstract

Deep convolutional neural networks (CNNs) have demonstrated impressive performance on visual object classification tasks. In addition, it is a useful model for predication of neuronal responses recorded in visual system. However, there is still no clear understanding of what CNNs learn in terms of visual neuronal circuits. Visualizing CNN's features to obtain possible connections to neuronscience underpinnings is not easy due to highly complex circuits from the retina to higher visual cortex. Here we address this issue by focusing on single retinal ganglion cells with a simple model and electrophysiological recordings from salamanders. By training CNNs with white noise images to predicate neural responses, we found that convolutional filters learned in the end are resembling to biological components of the retinal circuit. Features represented by these filters tile the space of conventional receptive field of retinal ganglion cells. These results suggest that CNN could be used to reveal structure components of neuronal circuits.

arXiv commentPresented at NIPS 2017 Symposium on Interpretable Machine Learning

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• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
• Deep Convolutional Neural Network Design Patterns

Cited by in corpus (4)

• Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks
• Neural System Identification with Spike-triggered Non-negative Matrix Factorization
• Towards the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes
• Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks