Locally optimal extracellular stimulation for chaotic desynchronization of neural populations

Abstract

We use optimal control theory to design a methodology to find locally optimal stimuli for desynchronization of a model of neurons with extracellular stimulation. This methodology yields stimuli which lead to positive Lyapunov exponents, and hence desynchronizes a neural population. We analyze this methodology in the presence of interneuron coupling to make predictions about the strength of stimulation required to overcome synchronizing effects of coupling. This methodology suggests a powerful alternative to pulsatile stimuli for deep brain stimulation as it uses less energy than pulsatile stimuli, and could eliminate the time consuming tuning process.

Stabilizing Quorum-Sensing Networks via Noise

“…This is the case, for example, of important applications such as neural networks, where pathological synchronization among bursting neurons might be related to the tremors observed in patients affected by the Parkinson's disease [27]. Interestingly, the key idea behind Deep Brain Stimulation techniques is indeed that of perturbing the synchronization of neurons via noise, see e.g., [28]. We also remark here that Theorem 2 offers an insight on how the size of the network has an impact on effects of noise diffusion.…”

Phase-dependent suppression of beta oscillations in Parkinson’s disease patients

“…Holt et al, 2016; D. Wilson & Moehlis, 2014). To address whether this mechanism could apply here, we investigated whether more phase slips, indicating discontinuities in the oscillation phase, were seen in the 15 ms following consecutive cycles of stimulation at the suppressing or amplifying phase (reported as a percentage of stimulus pulses) ( Fig.…”

“…Advancements to DBS algorithms have been aimed at improving efficacy and reducing the amount of current delivered, which could limit stimulation-induced side effects and conserve battery power (Adamchic et al, 2014;Brocker et al, 2017;Cagnan et al, 2017;Little et al, 2013;D. Wilson & Moehlis, 2014).…”

Neurocontrol: Methods, models and technologies for manipulating dynamics in the brain

“…Unlike other proposed methods in [106], [110], the procedure does not need the full model of the dynamics. Later, the methodology was moved closer towards experimentation (adapted for extracellular neural stimulation as is the case of DBS) in [111].…”