Closed Loop Deep Brain

Closed-loop deep brain stimulation (CL-DBS) aims to improve the treatment of neurological disorders like Parkinson's disease by dynamically adjusting electrical stimulation based on real-time brain activity. Current research focuses on developing more energy-efficient CL-DBS systems, employing reinforcement learning algorithms (like TD3 and A2C) and neuromorphic architectures (e.g., using Leaky Integrate and Fire neurons) to optimize stimulation parameters and minimize side effects. This work is significant because it promises to enhance the efficacy and longevity of DBS therapies, potentially leading to improved quality of life for patients and reducing the need for frequent device replacements. Furthermore, the development of CL-DBS systems is advancing our understanding of brain-computer interfaces and the complex interplay between neural activity and external stimulation.