  Parkinson's disease is marked by altered and increased firing characteristics
of pathological oscillations in the brain. In other words, it causes abnormal
synchronous oscillations and suppression during neurological processing. In
order to examine and regulate the synchronization and pathological oscillations
in motor circuits, deep brain stimulators (DBS) are used. Although machine
learning methods have been applied for the investigation of suppression, these
models require large amounts of training data and computational power, both of
which pose challenges to resource-constrained DBS. This research proposes a
novel reinforcement learning (RL) framework for suppressing the synchronization
in neuronal activity during episodes of neurological disorders with less power
consumption. The proposed RL algorithm comprises an ensemble of a temporal
representation of stimuli and a twin-delayed deep deterministic (TD3) policy
gradient algorithm. We quantify the stability of the proposed framework to
noise and reduced synchrony using RL for three pathological signaling regimes:
regular, chaotic, and bursting, and further eliminate the undesirable
oscillations. Furthermore, metrics such as evaluation rewards, energy supplied
to the ensemble, and the mean point of convergence were used and compared to
other RL algorithms, specifically the Advantage actor critic (A2C), the Actor
critic with Kronecker-featured trust region (ACKTR), and the Proximal policy
optimization (PPO).

Machine Learning;Artificial Intelligence;Systems and Control;Systems and Control;Neurons and Cognition