Saiga Rino, Shiga Kaede, Maruta Yo, Inomoto Chie, Kajiwara Hiroshi, Nakamura Naoya, Kakimoto Yu, Yamamoto Yoshiro, Yasutake Masahiro, Uesugi Masayuki, Takeuchi Akihisa, Uesugi Kentaro, Terada Yasuko, Suzuki Yoshio, Nikitin Viktor, De Andrade Vincent, De Carlo Francesco, Yamashita Yuichi, Itokawa Masanari, Ide Soichiro, Ikeda Kazutaka, Mizutani Ryuta
Department of Bioengineering, Tokai University, Hiratsuka, Kanagawa, 259-1292, Japan.
Department of Pathology, Tokai University School of Medicine, Isehara, Kanagawa, 259-1193, Japan.
Sci Rep. 2025 Jul 11;15(1):25091. doi: 10.1038/s41598-025-10912-3.
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.
小鼠和人类大脑具有依赖于其神经网络的不同功能。我们分析了小鼠内侧前额叶皮质脑组织的纳米级三维结构，并将其与人类前扣带回皮质的结构进行了比较。所得结果表明，小鼠神经元的胞体比人类神经元的小，神经突也比人类神经元的细。我们在生成对抗网络（GAN）和去噪扩散隐式模型（DDIM）的卷积层中实现了小鼠神经元的这些特征，然后使用猫脸、奶酪、人脸、鸟类和汽车的照片数据集对其进行图像生成任务。在使用猫脸和奶酪照片数据集的图像生成任务中，模仿小鼠的GAN优于标准GAN，但在生成人脸和鸟类图像时表现较差。模仿小鼠的DDIM给出了类似的结果，表明数据集的性质影响了结果。对这五个数据集的分析表明它们的图像熵存在差异，这应该会影响图像生成所需的参数数量。模仿小鼠的人工智能的偏好与通常与小鼠相关的印象一致。应通过在人工神经网络中应用其他生物学发现来研究神经网络与脑功能之间的关系。
