使用数据驱动的SINDy算法发现随机决策模型的动态方程
Brendan Lenfesty1, Saugat Bhattacharyya2, KongFatt Wong-Lin3
1Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, BT48 7JL Derry-Londonderry, Northern Ireland, U.K. lenfesty-b@ulster.ac.uk.
Neural computation
|January 9, 2025
概括
本研究引入了稀疏识别非线性动态 (SINDy) 来建模决策动态. 从神经活动中有效估计决策变量和模型参数,推进感知决策研究.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 动态系统 动态系统
背景情况:
- 感知决策依赖于随着时间的推移积累的感官证据.
- 序列采样模型描述了这一过程,由神经活动跟踪的决策变量.
- 目前分析决策动态的计算方法有限.
研究的目的:
- 应用非线性动态的稀疏识别 (SINDy) 来发现随机决策模型的决定性组成部分.
- 评估SINDy在估计模型参数和预测模拟神经数据行为的有效性.
- 探索SINDy对分析感知决策动态的实用性.
主要方法:
- 使用了非线性动态的稀疏识别 (SINDy),这是一个数据驱动的方法.
- 应用SINDy对模拟的决策变量活动从反应时间任务.
- 研究了多试验,试验平均和单试验SINDy方法,假设已知的噪声系数.
主要成果:
- 在动态方程中,SINDy成功估计了动态方程,选择精度和各种信号噪声比率的决策时间中的确定性术语.
- 多试验的SINDy方法产生了最佳的性能.
- 单一试验SINDy显示了实时建模的潜力.
结论:
- SINDy提供了一种强大的数据驱动方法,用于阐明感知决策的动态.
- 这些发现为分析使用SINDy.的首次通道时间问题提供了替代方法.
- 这项工作推进了用于理解选择神经机制的计算方法.
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