Structural differences between human and mouse neurons and their implementation in generative AIs

Structural differences between human and mouse neurons and their implementation in generative AIs

Abstract Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained resu...

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Main Authors: Rino Saiga, Kaede Shiga, Yo Maruta, Chie Inomoto, Hiroshi Kajiwara, Naoya Nakamura, Yu Kakimoto, Yoshiro Yamamoto, Masahiro Yasutake, Masayuki Uesugi, Akihisa Takeuchi, Kentaro Uesugi, Yasuko Terada, Yoshio Suzuki, Viktor Nikitin, Vincent De Andrade, Francesco De Carlo, Yuichi Yamashita, Masanari Itokawa, Soichiro Ide, Kazutaka Ikeda, Ryuta Mizutani
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
nano-CT
Neuron
Generative AI
Online Access:https://doi.org/10.1038/s41598-025-10912-3
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Summary:Abstract Mouse and human brains have different functions that depend on their neuronal networks. We analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. We implemented these characteristics of mouse neurons in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, birds, and automobiles. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the five datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.
ISSN:2045-2322

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