Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Nat Neurosci. 2024 Oct;27(10):2033-2045. doi: 10.1038/s41593-024-01731-2. Epub 2024 Sep 6.
Understanding the dynamical transformation of neural activity to behavior requires new capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural dynamics and test hypotheses about the origin of nonlinearity. We present dissociative prioritized analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilities with a multisection neural network architecture and training approach. Analyzing cortical spiking and local field potential activity across four movement tasks, we demonstrate five use-cases. DPAD enabled more accurate neural-behavioral prediction. It identified nonlinear dynamical transformations of local field potentials that were more behavior predictive than traditional power features. Further, DPAD achieved behavior-predictive nonlinear neural dimensionality reduction. It enabled hypothesis testing regarding nonlinearities in neural-behavioral transformation, revealing that, in our datasets, nonlinearities could largely be isolated to the mapping from latent cortical dynamics to behavior. Finally, DPAD extended across continuous, intermittently sampled and categorical behaviors. DPAD provides a powerful tool for nonlinear dynamical modeling and investigation of neural-behavioral data.
理解神经活动到行为的动态转变需要新的能力来非线性地建模、区分和优先考虑与行为相关的神经动力学，并测试关于非线性起源的假设。我们提出了动态分离优先分析（DPAD），这是一种非线性动力学建模方法，通过多节神经网络架构和训练方法实现了这些能力。通过分析四个运动任务的皮质尖峰和局部场电位活动，我们展示了五个用例。DPAD 能够实现更准确的神经行为预测。它确定了局部场电位的非线性动力学转换，这些转换比传统的功率特征更具行为预测性。此外，DPAD 实现了行为预测性非线性神经降维。它能够对神经行为转换中的非线性进行假设检验，结果表明，在我们的数据集，非线性可以主要隔离到从潜在皮质动力学到行为的映射。最后，DPAD 扩展到连续、间歇性采样和分类行为。DPAD 为非线性动力学建模和神经行为数据的研究提供了强大的工具。
