Efficient and robust control using spiking neural networks (SNNs) is still an open problem. Whilst behaviour of biological agents is produced through sparse and irregular spiking patterns, which provide both robust and efficient control, the activity patterns in most artificial spiking neural networks used for control are dense and regular -- resulting in potentially less efficient codes. Additionally, for most existing control solutions network training or optimization is necessary, even for fully identified systems, complicating their implementation in on-chip low-power solutions. The neuroscience theory of Spike Coding Networks (SCNs) offers a fully analytical solution for implementing dynamical systems in recurrent spiking neural networks -- while maintaining irregular, sparse, and robust spiking activity -- but it's not clear how to directly apply it to control problems. Here, we extend SCN theory by incorporating closed-form optimal estimation and control. The resulting networks work as a spiking equivalent of a linear-quadratic-Gaussian controller. We demonstrate robust spiking control of simulated spring-mass-damper and cart-pole systems, in the face of several perturbations, including input- and system-noise, system disturbances, and neural silencing. As our approach does not need learning or optimization, it offers opportunities for deploying fast and efficient task-specific on-chip spiking controllers with biologically realistic activity.

翻译：利用脉冲神经网络实现高效鲁棒的控制仍是一个开放性问题。尽管生物体的行为通过稀疏且不规则的脉冲模式产生，既能提供鲁棒控制又能实现高效编码，但大多数用于控制的人工脉冲神经网络中的活动模式却密集且规则——导致编码效率可能较低。此外，对于现有的多数控制方案，即使系统完全已知，仍需要进行网络训练或优化，这增加了在片上低功耗方案中实现的复杂性。脉冲编码网络（Spike Coding Networks, SCNs）的神经科学理论提供了一种在全解析框架下通过递归脉冲神经网络实现动力系统的方案——同时保持不规则、稀疏且鲁棒的脉冲活动——但如何将其直接应用于控制问题尚不明确。本文通过融入闭式最优估计与控制理论扩展了SCN理论。由此生成的网络可作为线性二次型高斯控制器的脉冲等价物。我们验证了该方法在弹簧-质量-阻尼系统和推车-倒立摆系统中面对多种扰动（包括输入噪声、系统噪声、系统扰动及神经元静默）时的鲁棒脉冲控制能力。由于该方法无需学习或优化，它为部署具有生物真实性活动的快速高效任务专用型片上脉冲控制器提供了新机遇。