Recovery of dynamics and function in spiking neural networks by closed-loop control

Abstract

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. Author SummaryBrain stimulation is being used to ease symptoms in several neurological disorders in cases where pharmacological treatment is not effective (anymore). The most common way for stimulation so far has been to apply a fixed, predetermined stimulus irrespective of the actual state of the brain or the condition of the patient. Recently, alternative strategies such as event-triggered stimulation protocols have attracted the interest of researchers. In these protocols the state of the affected brain area is continuously monitored, but the stimulus is only applied if certain criteria are met. Here we go one step further and present a truly closed-loop stimulation protocol. That is, a stimulus is being continuously provided and the magnitude of the stimulus depends, at any point in time, on the ongoing neural activity dynamics of the affected brain area. This results not only in suppression of the pathological activity, but also in a partial recovery of the transfer function of the activity dynamics. Thus, the ability of the lesioned brain area to carry out relevant computations is restored up to a point as well. PLOS Computational Biology |

Bioelectric Medicine and Devices for the Treatment of Spinal Cord Injury

“…Similarly, closed-loop ES systems are being developed for Parkinson's disease [Beuter et al, 2014] and stroke patients [Fujiwara et al, 2009], and have demonstrated to be more effective deep-brain stimulation parameters when combined with hippocampal theta oscillations as a feedback input [Wu et al, 2015]. Furthermore, the development of closed-loop system algorithms for improved neuromodulation has gained recent attention [Liu et al, 2011;Vlachos et al, 2016].…”

Adaptive delayed feedback control disrupts unwanted neuronal oscillations and adjusts to network synchronization dynamics

“…This method has interesting properties that make it attractive for neuromodulation applications: it does not require prior knowledge about the controlled system, the actuation signal vanishes as the system approaches the desired trajectory (be it a stable orbit or its suppression), it is simple and easy to implement both in software and hardware and the eventual technical latencies can be compensated by adjusting the delay accordingly. DFC has been extensively applied in the context of neuroscience, with studies focusing on specific disorders such as Parkinson's Disease [15][16][17][18][19][20] and epilepsy 21 . However, there is still controversy in the literature regarding its efficacy and whether it may instead promote synchrony depending on the baseline levels of synchrony of the modelled network 22 .…”