By training CNNs with white noise images to predict neuronal responses, we found that fine structures of the retinal receptive field can be revealed.

Abstract—Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for ...

Abstract—Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for ...

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 ...

CNNs could be used to reveal structure components of neuronal circuits, and provide a powerful model for neural system identification, by training CNNs with ...

Mar 16, 2020 · Here, we address this issue by focusing on single retinal ganglion cells with biophysical models and recording data from animals. By training ...

This is for using CNNs as a model to study the receptive fields of the retinal cells. See the publication: Yan Q, Zheng Y, Jia S, Zhang Y, Yu Z, Chen F, ...

Revealing structure components of the retina by deep learning networks ... Revealing Fine Structures of the Retinal Receptive Field by Deep-Learning Networks.

Convolutional neural networks provide accurate models of the retinal response ... CNN internal units have receptive field structure matching retinal interneurons.

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