Correspondence between representations in visual cortices and neural networks

February 08, 2021

This discovery was made possible by applying the research method for the comparison of the brain activity between monkeys and humans to artificial neural networks. This finding might be helpful not only to understand the cortical mechanism of attentional selection but also to develop artificial intelligence.

Deep neural networks (DNNs), which are used in the development of artificial intelligence, are mathematical models for obtaining appropriate mechanisms to solve specific problems from the training with a large-scale dataset. However, the detailed mechanisms underlying DNNs through this learning process have not yet been clarified.

A research group led by Nobuhiko Wagatsuma, Lecturer at the Faculty of Science, Toho University, Akinori Hidaka, Associate Professor at the Faculty of Science and Engineering, Tokyo Denki University, and Hiroshi Tamura, Associate Professor at the Graduate School of Frontiers Biosciences, Osaka University, found that the characteristics of responses in DNNs for predicting the attention to the most important location in images were consistent with those of the neural representation in the primary visual cortex (V1) of primates. The discovery was made possible by applying the analysis method designed for comparing the characteristics of the neuronal activity in monkeys with that in humans to DNNs.

The result of this study provides important insight into the neural mechanism of attention. Additionally, the application of the attentional mechanism in the primates including human may accelerate the development of artificial intelligence.

Key Points:
These results have been published in eNeuro, an open-access journal of Society for Neuroscience (SfN), headquartered in the United States, on November 24, 2020.

Toho University

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