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

Abstract

Adaptive neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinsons disease and epilepsy. However, standard stimulation protocols mostly rely on continuous open-loop stimulation. We implement here, for the first time in neuronal populations, two different Delayed Feedback Control (DFC) algorithms and assess their efficacy in disrupting unwanted neuronal oscillations. DFC is a well-established closed-loop control technique but its use in neuromodulation has been limited so far to models and computational studies. Leveraging on the high spatiotemporal monitoring capabilities of specialized in vitro platforms, we show that standard DFC in fact worsens the neuronal population oscillatory behaviour and promotes faster bursting, which was never reported in silico. Alternatively, we present adaptive DFC (aDFC) that monitors ongoing oscillation periodicity and self-tunes accordingly. aDFC disrupts collective neuronal oscillations and decreases network synchrony. Furthermore, we show that the intrinsic population dynamics have a strong impact in the susceptibility of networks to neuromodulation. Experimental data was complemented with computer simulations to show how this network controllability might be determined by specific network properties. Overall, these results support aDFC as a better candidate for therapeutic neurostimulation and provide new insights regarding the controllability of neuronal systems.