Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089.
Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089.
Proc Natl Acad Sci U S A. 2024 Feb 13;121(7):e2212887121. doi: 10.1073/pnas.2212887121. Epub 2024 Feb 9.
Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or inputs from other brain regions. To avoid misinterpreting temporally structured inputs as intrinsic dynamics, dynamical models of neural activity should account for measured inputs. However, incorporating measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is important for studying neural computations of behavior. We first show how training dynamical models of neural activity while considering behavior but not input or input but not behavior may lead to misinterpretations. We then develop an analytical learning method for linear dynamical models that simultaneously accounts for neural activity, behavior, and measured inputs. The method provides the capability to prioritize the learning of intrinsic behaviorally relevant neural dynamics and dissociate them from both other intrinsic dynamics and measured input dynamics. In data from a simulated brain with fixed intrinsic dynamics that performs different tasks, the method correctly finds the same intrinsic dynamics regardless of the task while other methods can be influenced by the task. In neural datasets from three subjects performing two different motor tasks with task instruction sensory inputs, the method reveals low-dimensional intrinsic neural dynamics that are missed by other methods and are more predictive of behavior and/or neural activity. The method also uniquely finds that the intrinsic behaviorally relevant neural dynamics are largely similar across the different subjects and tasks, whereas the overall neural dynamics are not. These input-driven dynamical models of neural-behavioral data can uncover intrinsic dynamics that may otherwise be missed.
神经动力学可以反映内在动力学或动态输入，例如感觉输入或来自其他脑区的输入。为了避免将具有时间结构的输入误解为内在动力学，神经活动的动力模型应该考虑测量的输入。然而，在联合神经行为数据的动力建模中，纳入测量的输入仍然难以实现，这对于研究行为的神经计算很重要。我们首先展示了在考虑行为但不考虑输入或仅考虑输入而不考虑行为的情况下训练神经活动动力模型可能导致的误解。然后，我们为线性动力模型开发了一种分析学习方法，该方法同时考虑了神经活动、行为和测量的输入。该方法提供了优先学习内在与行为相关的神经动力学的能力，并将其与其他内在动力学和测量输入动力学区分开来。在具有执行不同任务的固定内在动力学的模拟大脑的数据中，该方法无论任务如何都能正确找到相同的内在动力学，而其他方法可能会受到任务的影响。在来自三个执行两个不同运动任务并具有任务指令感觉输入的主体的神经数据集上，该方法揭示了其他方法错过的低维内在神经动力学，并且对行为和/或神经活动具有更好的预测性。该方法还独特地发现，内在与行为相关的神经动力学在不同的主体和任务中基本相似，而整体神经动力学则不然。这些神经行为数据的输入驱动动力模型可以揭示可能被忽视的内在动力学。
