Fehrman Christof, Meliza C Daniel
Department of Mechanical Engineering and Materials Science, Duke University, Durham NC 27708, USA.
Department of Psychology, University of Virginia, Charlottesville VA 22904, USA.
ArXiv. 2025 Aug 11:arXiv:2406.14801v2.
Neural manifolds are an attractive theoretical framework for characterizing the complex behaviors of neural populations. However, many of the tools for identifying these low-dimensional subspaces are correlational and provide limited insight into the underlying dynamics. The ability to precisely control the latent activity of a circuit would allow researchers to investigate the structure and function of neural manifolds. We simulate controlling the latent dynamics of a neural population using closed-loop, dynamically generated sensory inputs. Using a spiking neural network (SNN) as a model of a neural circuit, we find low-dimensional representations of both the network activity (the neural manifold) and a set of salient visual stimuli. The fields of classical and optimal control offer a range of methods to choose from for controlling dynamics on the neural manifold, which differ in performance, computational cost, and ease of implementation. Here, we focus on two commonly used control methods: proportional-integral-derivative (PID) control and model predictive control (MPC). PID is a computationally lightweight controller that is simple to implement. In contrast, MPC is a model-based, anticipatory controller with a much higher computational cost and engineering overhead. We evaluate both methods on trajectory-following tasks in latent space, under partial observability and in the presence of unknown noise. While both controllers in some cases were able to successfully control the latent dynamics on the neural manifold, MPC consistently produced more accurate control and required less hyperparameter tuning. These results demonstrate how MPC can be applied on the neural manifold using data-driven dynamics models, and provide a framework to experimentally test for causal relationships between manifold dynamics and external stimuli.
神经流形是用于刻画神经群体复杂行为的一个有吸引力的理论框架。然而，许多用于识别这些低维子空间的工具都是相关性的，并且对潜在动力学的洞察有限。精确控制回路潜在活动的能力将使研究人员能够研究神经流形的结构和功能。我们模拟使用闭环、动态生成的感官输入来控制神经群体的潜在动力学。使用脉冲神经网络（SNN）作为神经回路的模型，我们找到了网络活动（神经流形）和一组显著视觉刺激的低维表示。经典控制和最优控制领域提供了一系列方法可供选择，用于控制神经流形上的动力学，这些方法在性能、计算成本和实现难易程度上有所不同。在这里，我们重点关注两种常用的控制方法：比例积分微分（PID）控制和模型预测控制（MPC）。PID是一种计算轻量级的控制器，易于实现。相比之下，MPC是一种基于模型的预测控制器，计算成本和工程开销要高得多。我们在潜在空间中的轨迹跟踪任务上评估这两种方法，包括部分可观测性和存在未知噪声的情况。虽然在某些情况下，两种控制器都能够成功控制神经流形上的潜在动力学，但MPC始终能产生更精确的控制，并且需要更少的超参数调整。这些结果展示了如何使用数据驱动的动力学模型将MPC应用于神经流形，并提供了一个框架来通过实验测试流形动力学与外部刺激之间的因果关系。
