在经过训练以执行上下文依赖任务的神经网络中出现模块化表示
W Jeffrey Johnston1,2, Stefano Fusi1,2,3
1Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
bioRxiv : the preprint server for biology
|October 17, 2024
概括
神经网络中的局部模块化支持以低维输入的上下文依赖行为,为更快的学习和概括创建抽象表示. 这挑战了解剖学的约束,为大脑功能提供了洞察力.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
- 认知科学是一种认知科学.
背景情况:
- 大脑在区域中表现出大规模的模块化,受连接性和物理几何学的影响.
- 关于单个大脑区域内是否存在专门的神经元子群 (模块) 是否存在争论的.
研究的目的:
- 研究人工神经网络中局部模块化的出现和功能.
- 确定模块化支持上下文依赖行为和学习的条件.
主要方法:
- 研究了不同输入维度的人工神经网络.
- 分析了在人口层面上模块化专业化的出现.
- 检查了模块化和表示性质之间的关系.
主要成果:
- 局部模块化在神经网络中出现,支持上下文依赖的行为,特别是低维输入,没有解剖学约束.
- 模块化专业化导致抽象的表示,使得快速的学习和对新和相关任务的概括.
- 非模块化表示方便更快地学习不相关的语境.
结论:
- 局部神经网络的模块化是由输入维度驱动的,并支持适应性行为.
- 结果协调了关于神经元专业化的相互矛盾的实验数据.
- 预测未来的实验途径来研究大脑模块化.
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