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BGRL: Basal Ganglia inspired Reinforcement Learning based framework for deep brain stimulators.

Harsh Agarwal1, Heena Rathore2

  • 1Department of Electrical and Computer Engineering, Indian Institute of Technology, India.

Artificial Intelligence in Medicine
|January 6, 2024
PubMed
Summary

A new Basal Ganglia inspired Reinforcement Learning (BGRL) method enhances Deep Brain Stimulation (DBS) by reducing neural synchrony. This closed-loop approach offers improved efficiency and suppression capabilities for neurological disorder treatment.

Keywords:
Actor–criticBasal gangliaDeep brain stimulatorsNeuro-inspiredReinforcement learning

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Area of Science:

  • Biomedical Engineering
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Deep Brain Stimulation (DBS) is an implantable device for neurological disorders.
  • Current DBS devices use fixed stimulation frequencies, limiting personalized treatment effectiveness.
  • Optimizing stimulation parameters is key for successful DBS therapy.

Purpose of the Study:

  • To introduce a novel Basal Ganglia inspired Reinforcement Learning (BGRL) approach for DBS.
  • To incorporate a closed-loop feedback mechanism for suppressing neural synchrony.
  • To enhance the efficacy and efficiency of DBS treatment through personalized stimulation.

Main Methods:

  • Developed a Basal Ganglia inspired Reinforcement Learning (BGRL) algorithm.
  • Integrated an actor-critic architecture from reinforcement learning (RL).
  • Implemented a closed-loop feedback system to dynamically adjust stimulation parameters.

Main Results:

  • BGRL significantly reduced synchronous electrical pulses compared to standard RL algorithms.
  • BGRL demonstrated superior suppression capabilities and lower energy consumption.
  • Suppressed synchronous electrical pulses by 40% (regular), 146% (chaotic), and 40% (bursting) versus the soft actor-critic model.

Conclusions:

  • The BGRL approach shows significant promise for suppressing neural synchrony in DBS.
  • BGRL offers an efficient, closed-loop alternative to traditional open-loop DBS methodologies.
  • This method paves the way for more personalized and effective neurological disorder treatments.