Closed-Loop Reinforcement Learning Based Deep Brain Stimulation Using SpikerNet: A Computational Model-Reference-Cited by-同舟云学术

Closed-Loop Reinforcement Learning Based Deep Brain Stimulation Using SpikerNet: A Computational Model

Published:2023-04-24 Issue: Volume: Page:
ISSN:
Container-title:2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)
language:
Short-container-title:

Author:

Coventry Brandon S.¹,Bartlett Edward L.²

Affiliation:

1. Weldon School of Biomedical Engineering and the Institute for Integrative Neuroscience, Purdue University,West Lafayette

2. Weldon School of Biomedical Engineering and the Institute for Integrative Neuroscience, Purdue University,Department of Biological Sciences,West Lafayette,IN,USA,49707

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10123380/10123710/10123797.pdf?arnumber=10123797

Reference21 articles.

1. Parkinsonian Beta Dynamics during Rest and Movement in the Dorsal Pallidum and Subthalamic Nucleus

2. Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson’s disease

3. A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments

4. The Safe Delivery of Electrical Currents and Neuromodulation

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution;eneuro;2024-06-25

2. Characterization and closed-loop control of infrared thalamocortical stimulation produces spatially constrained single-unit responses;PNAS Nexus;2024-02-01

3. Spatially specific, closed-loop infrared thalamocortical deep brain stimulation;2023-10-06