单单元激活为认知任务的新出现的电路解决方案赋予了感应偏差
Pavel Tolmachev1, Tatiana A Engel1
1Princeton Neuroscience Institute, Princeton University, Princeton, NJ USA.
Nature machine intelligence
|October 27, 2025
概括
在循环神经网络 (RNN) 中,不同的激活函数为认知任务创建不同的神经表征和电路解决方案. 这些架构选择显著影响了概括,挑战了它们不会影响任务结果的假设.
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.
- 机器学习是机器学习.
背景情况:
- 循环神经网络 (RNN) 被广泛用于模拟大脑动态和人口级计算.
- 通常认为,在RNN单元中选择非线性激活函数不会影响新出现的任务解决方案.
研究的目的:
- 研究RNN中单单元激活函数如何影响神经表征,动态和任务解决方案.
- 为了确定不同的激活函数是否导致质量上不同的电路解决方案和概括行为.
主要方法:
- 使用训练有素的循环神经网络 (RNN),具有不同的非线性激活功能.
- 采用模型蒸方法来分析神经表示和动态的差异.
- 对分布之外的输入进行评估的概括性能.
主要成果:
- 单单元激活函数强加诱导偏差,塑造神经群体轨迹,单单元选择性和固定点配置.
- 不同的激活函数导致认知任务的质量不同的电路解决方案.
- 这些差异导致了对未见数据的各种泛化行为.
结论:
- 激活函数的选择不是一个小的建筑细节,而是赋予诱导偏差的重要因素.
- 不同的RNN架构为认知任务提供了不同的解决方案,影响了概括.
- 这项研究提出了关于哪些RNN架构最好模拟任务执行的生物神经机制的问题.
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