任务参数化动力学:在循环神经网络中代表时间和决策
Cecilia Jarne1,2,3,4, Ryeongkyung Yoon5, Tahra Eissa6,7
1Universidad Nacional de Quilmes, Argentina.
bioRxiv : the preprint server for biology
|September 26, 2025
概括
循环神经网络 (RNN) 使用不同的动态,如振荡或集成,来表示延迟反应的过期时间. 这种时间表示是全人口的,不是局部的,并且与任务输出协调.
科学领域:
- 计算神经科学是一种计算神经科学.
- 机器学习是机器学习.
- 神经网络的神经网络的神经网络
背景情况:
- 循环神经网络 (RNN) 对于模拟人工和生物系统中的时间动态至关重要.
- 了解RNN如何表示已过的时间是解读它们在学习延迟和决策中的作用的关键.
研究的目的:
- 调查RNN用来表示定时响应的过期时间的内部机制.
- 探索网络动态和连接如何为RNN中的时间计算做出贡献.
主要方法:
- 在不同复杂度的延迟决策任务上培训RNN.
- 使用自身值光谱,连接分析和人口轨迹分析分析网络动态.
- 将神经轨迹与输出权重相关联,以评估读取机制.
主要成果:
- 在RNN中确定了不同的动态模式 (时间的振荡,积累的整合).
- 观察到时间和任务变量的全人口表示,而不是专门的单位.
- 在决策点附近的神经表达和输出读取之间展示了任务驱动的协调.
结论:
- RNN可以利用各种动态策略,包括集成和振荡,来编码时间信息.
- 在RNN中结构化的连接支持时间计算的灵活和冗余的解决方案,反映生物原理.
- 研究结果提供了关于人工神经网络如何解决复杂的时间问题的见解.
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