用人工神经网络建模决策的自然神经网络
Akihiro Funamizu1, Ryo Karakida2
1Institute for Quantitative Biosciences, the University of Tokyo, Laboratory of Neural Computation, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan; Department of Life Sciences, Graduate School of Arts and Sciences, the University of Tokyo, 3-8-1, Komaba, Meguro-ku, Tokyo 153-8902, Japan.
Neuroscience research
|September 28, 2025
概括
研究人员正在使用人工神经网络 (ANN),特别是循环神经网络 (RNN),来模拟用于决策的脑功能. 一个新的混合网络将真实小鼠神经元与RNN集成,以改善连续运动生成.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 神经科学和人工智能整合
背景情况:
- 了解大脑的决策过程需要研究大脑皮层,小脑和基底等区域的神经回路.
- 机器学习,特别是人工神经网络 (ANN),为模拟这些复杂的神经回路提供了强大的工具.
- 循环神经网络 (RNN) 是一种 ANN 的关键类型,非常适合在神经处理中建模时间动态.
研究的目的:
- 审查RNN在与决策任务相关的皮质功能建模中的应用.
- 探索将人工神经网络与生物神经网络进行比较的方法.
- 引入一种新的混合神经AI方法,将生物神经元与人工单元集成在一起.
主要方法:
- 审查关于RNNs的现有文献,以在前额叶和后额叶皮层中建模决策.
- 解释用于比较人工和生物神经网络活动和电路的技术.
- 开发和评估一个真实网络混合网络,将鼠标神经元活动与RNN单元集成在一起.
主要成果:
- RNN有效地模拟短期记忆,感知决策和基于价值的决策任务.
- 与纯粹的人工RNN相比,混合网络显示了与纯人工RNN相比,改进了连续时间的身体运动生成.
- 混合方法通过整合真实神经元,便于直接比较生物和人工网络活动.
结论:
- RNN是了解决策背后的神经机制的有价值的工具.
- 混合神经AI网络为更具生物现实的脑功能建模提供了一个有希望的途径.
- 结合神经科学和人工智能的综合性方法对于推进我们对自然和人工智能的理解至关重要.
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