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:使用神经神经网络(SNNS)的高效和稳健控制仍然是一个尚未解决的问题。虽然生物制剂的行为是通过稀疏和不规则的喷雾模式产生的,这些模式既提供了稳健又有效的控制,但用于控制的大多数人工喷雾神经网络的活动模式却十分密集和经常 -- -- 由此产生了潜在的效率较低的代码。此外,对于大多数现有的控制解决方案来说,网络培训或优化是必要的,即使是完全确定的系统也是如此,使得其在芯片低功率解决方案中的实施更加复杂。Spik Coding网络(SCNs)的神经科学理论为在经常性喷雾神经网络中实施动态系统提供了充分的分析解决方案,同时保持了不规则、稀少和强劲的喷雾活动 -- -- 但不清楚如何直接将其应用于控制问题。在这里,我们扩展了SCN的理论,将封闭式最佳估计和控制纳入。由此形成的网络作为相当于线性夸度-GAussian控制器的系统。我们展示了对模拟弹簧压和制式神经系统的严格控制,在面临一些不定期、不固定式的神经活动,包括快速的系统学习机会。