发表机构
Center for Neurocomputation and Machine Intelligence, Wu Tsai Institute; Department of Biomedical Engineering; Yale University（吴蔡研究所神经计算与机器智能中心; 生物医学工程系; 耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对神经活动多时间尺度识别难题，提出多时间尺度切换线性动力系统（MTS-SLDS），结合多滞后矩初始化与状态条件拉普拉斯EM推断，从潜在转移矩阵特征值提取时间尺度，在合成和神经实验中准确恢复时间尺度与切换结构。
AI 中文摘要
神经活动常常表现出多种时间尺度，这些时间尺度可能随行为状态和任务条件而变化。从神经记录中识别这些时间尺度对于更好地理解神经计算和功能至关重要。然而，基于自相关拟合的传统方法难以扩展到高维群体记录，并且当神经动力学随行为变化时可能变得不可靠。状态空间模型通过潜在动力系统对高维神经群体活动进行建模，已成为一个强大的框架，但标准公式和推断方法并未明确考虑多种时间尺度，因此不能保证准确恢复潜在的时间结构。受这些问题的启发，我们引入了多时间尺度切换线性动力系统（MTS-SLDS），这是一个从连续或尖峰神经观测中识别特定状态潜在时间尺度的框架。MTS-SLDS结合了多滞后矩初始化（可捕获跨多个观测滞后的时间结构）与\textit{状态条件}拉普拉斯期望最大化推断（可减少不确定状态下动力学统计量的混合）。特征时间尺度可直接从学习到的潜在转移矩阵的特征值中提取。在具有高斯和泊松尖峰观测的合成及神经实验中，MTS-SLDS在多个数据集上准确恢复了时间尺度和切换结构。
英文摘要
Neural activity often exhibits multiple timescales that can vary with behavioral states and task conditions. Identifying these timescales from neural recordings is important for better understanding neural computation and function. However, traditional approaches based on autocorrelation fitting are difficult to scale to high-dimensional population recordings and can become unreliable when neural dynamics change with behavior. State-space models have been a powerful framework for modeling high-dimensional neural population activity through latent dynamical systems, but standard formulations and inference methods do not explicitly account for multiple timescales and therefore do not guarantee accurate recovery of the underlying temporal structure. Motivated by these questions, we introduce the Multi-Timescale Switching Linear Dynamical System (MTS-SLDS), a framework for identifying regime-specific latent timescales from continuous or spiking neural observations. MTS-SLDS combines a multi-lag moment initialization, which captures temporal structure across multiple observation lags, with \textit{regime-conditioned} Laplace-EM inference, which reduces mixing of dynamical statistics across uncertain regimes. Characteristic timescales can then be extracted directly from the eigenvalues of the learned latent transition matrices. In synthetic and neural experiments with Gaussian and Poisson spike observations, MTS-SLDS accurately recovers timescales and switching structure over multiple datasets.
Comments30 pages, 10 figures
