While we can readily observe and model the dynamics of our limbs, analyzing the neurons that drive movement is not nearly as straightforward. As a result, their role in motor behavior (e.g., forward models, state estimators, controllers, etc.) remains elusive. Computational explanations of electrophysiological data often rely on firing rate models or deterministic spiking models. Yet neither can accurately describe the interactions of neurons that issue spikes, probabilistically. Here we take a normative approach by designing a probabilistic spiking network to implement LQR control for a limb model. We find typical results: cosine tuning curves, population vectors that correlate with reaching directions, low-dimensional oscillatory activity for reaches that have no oscillatory movement, and changes in neuron's tuning curves after force field adaptation. Importantly, while the model is consistent with these empirically derived correlations, we can also analyze it in terms of the known causal mechanism: an LQR controller and the probability distributions of the neurons that encode it. Redesigning the system under a different set of assumptions (e.g. a different controller, or network architecture) would yield a new set of testable predictions. We suggest this normative approach can be a framework for examining the motor system, providing testable links between observed neural activity and motor behavior.

中文翻译：

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神经运动控制的规范方法。

尽管我们可以方便地观察和模拟肢体的动力学，但是分析驱动运动的神经元并不是那么简单。结果，它们在运动行为中的作用（例如，前向模型，状态估计器，控制器等）仍然难以捉摸。电生理数据的计算解释通常取决于发射速率模型或确定性峰值模型。然而，都无法准确地描述概率性地发出尖峰的神经元的相互作用。在这里，我们通过设计一个概率峰值网络来实现肢体模型的LQR控制，从而采用一种规范性方法。我们发现典型的结果：余弦调谐曲线，与到达方向相关的总体向量，没有振荡运动的到达区域的低维振荡活动以及神经元的变化 力场适应后的s调整曲线。重要的是，虽然该模型与这些经验得出的相关性一致，但我们还可以根据已知的因果机制进行分析：一个LQR控制器和对其进行编码的神经元的概率分布。在一组不同的假设（例如，不同的控制器或网络体系结构）下重新设计系统将产生一组新的可测试预测。我们建议这种规范方法可以作为检查运动系统的框架，在观察到的神经活动和运动行为之间提供可测试的联系。在一组不同的假设（例如，不同的控制器或网络体系结构）下重新设计系统将产生一组新的可测试预测。我们建议这种规范性方法可以作为检查运动系统的框架，在观察到的神经活动和运动行为之间提供可测试的联系。在一组不同的假设（例如，不同的控制器或网络体系结构）下重新设计系统将产生一组新的可测试预测。我们建议该规范方法可以作为检查运动系统的框架，在观察到的神经活动和运动行为之间提供可测试的联系。