Electrical synapses for a pooling layer of the convolutional neural network in retinas.

This article explores the use of convolutional neural networks (CNN) in studying retinal neural networks. It discusses the application of CNN as a framework for analyzing retinal circuits and examines the relationship between structural parameters and information-processing functions. The article focuses on the primary rod signal pathway in mouse and macaque retinas, particularly electrical synapses. By comparing the differences in retinal circuitry between these species, the author suggests that insights into neurocircuitry principles and adaptational designs can be gained. The article also discusses the methodology for observing neural circuitry using electron microscopy and emphasizes the importance of human pattern recognition in interpreting the images. It concludes by stating that connectomic studies on visual processing can enhance our understanding of natural intelligence. The document includes ethical guidelines, financial support, acknowledgments, a conflict of interest statement, and references to related research papers. [Extracted from the article]