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换需要新的能力，以非线性建模、解离和优先考虑与行为相关的神经动力学，并检验关于非线性起源的假设。我们提出了分离动力学优先分析 （DPAD），这是一种非线性动力学建模方法，可通过多部分神经网络架构和训练方法实现这些功能。通过分析四个运动任务中的皮质尖峰和局部场电位活动，我们演示了五个用例。DPAD 实现了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变换，这些变换比传统的电源特征更具行为预测性。此外，DPAD 实现了行为预测非线性神经降维。它使关于神经行为转换中的非线性的假设检验成为可能，揭示了在我们的数据集中，非线性在很大程度上可以被隔离到从潜在皮层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。