Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.

学界对开发新型脑刺激方法的热情日益高涨，此类方法既可以调控疾病相关的异常神经活动，又能助力基础神经科学问题的探索。传统脑活动调控手段多采用开环（open-loop）策略，这类方法通常会引发过度刺激，且尤为关键的是，无法恢复神经网络原本执行的计算过程，因此往往伴随诸多非预期的副作用。为此，我们提出延迟反馈控制（delayed feedback control, DFC）这一概念简洁却行之有效的方法，用于调控脉冲神经网络（spiking neural networks, SNNs）中的病理振荡活动。通过数学分析与数值模拟，我们证实延迟反馈控制可通过抑制或增强同步不规则活动，恢复多种异常的神经网络动态特性。尤为重要的是，延迟反馈控制不仅能将系统引导至健康状态，还可恢复底层神经网络原本的计算功能。最后，依托本研究提出的理论，我们阐明了单个神经元与突触特性在决定闭环（closed-loop）系统稳定性中的核心作用。