{"title":"Modeling natural neural networks of decision making with artificial neural networks","authors":"Akihiro Funamizu , Ryo Karakida","doi":"10.1016/j.neures.2025.104961","DOIUrl":null,"url":null,"abstract":"<div><div>One main focus in neuroscience is to understand the relationship between decision making and various brain regions. Researchers use machine learning approaches to model the neural circuits of cerebral cortices, cerebellum, and basal ganglia. This review focuses on artificial neural networks (ANNs), particularly recurrent neural networks (RNNs), to model cortical functions for decision making. We first introduce the basic architecture of RNNs and explain how researchers compare the activity and circuits between artificial and biological networks. We also summarize how RNNs model the prefrontal and posterior parietal cortical in tasks involving short-term memory, perceptual decision making, and value-based decision making. We then show our recent challenges to develop a real-cyber hybrid network, that integrates neuronal activity in mice with RNN-based artificial units to better generate continuous-time body movements, compared to conventional RNNs that only use artificial units. The hybrid network tries to develop RNNs which have similar activity to the brain by using real neurons, rather than developing artificial RNNs and comparing their functions with biological brain. We propose that such integrative approaches in neuroscience and AI will further our understanding of both natural and artificial intelligence in the field of neuro-AI.</div></div>","PeriodicalId":19146,"journal":{"name":"Neuroscience Research","volume":"220 ","pages":"Article 104961"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroscience Research","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168010225001440","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
One main focus in neuroscience is to understand the relationship between decision making and various brain regions. Researchers use machine learning approaches to model the neural circuits of cerebral cortices, cerebellum, and basal ganglia. This review focuses on artificial neural networks (ANNs), particularly recurrent neural networks (RNNs), to model cortical functions for decision making. We first introduce the basic architecture of RNNs and explain how researchers compare the activity and circuits between artificial and biological networks. We also summarize how RNNs model the prefrontal and posterior parietal cortical in tasks involving short-term memory, perceptual decision making, and value-based decision making. We then show our recent challenges to develop a real-cyber hybrid network, that integrates neuronal activity in mice with RNN-based artificial units to better generate continuous-time body movements, compared to conventional RNNs that only use artificial units. The hybrid network tries to develop RNNs which have similar activity to the brain by using real neurons, rather than developing artificial RNNs and comparing their functions with biological brain. We propose that such integrative approaches in neuroscience and AI will further our understanding of both natural and artificial intelligence in the field of neuro-AI.
期刊介绍:
The international journal publishing original full-length research articles, short communications, technical notes, and reviews on all aspects of neuroscience
Neuroscience Research is an international journal for high quality articles in all branches of neuroscience, from the molecular to the behavioral levels. The journal is published in collaboration with the Japan Neuroscience Society and is open to all contributors in the world.