scholarly journals Selective modulation of population dynamics during neuroprosthetic skill learning

2021 ◽  
Author(s):  
Ellen L. Zippi ◽  
Albert K. You ◽  
Karunesh Ganguly ◽  
Jose M. Carmena

Learning to control a brain-machine interface (BMI) is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-rated changes in cortical population dynamics within these groups compare. To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this coordination was primarily driven by changes in the direct subpopulation while the indirect subpopulation remained relatively stable. These findings indicate that motor cortex refines cortical dynamics throughout the entire network during learning, with a more pronounced effect in ensembles causally linked to behavior.

Author(s):  
Qiaosheng Zhang ◽  
Sile Hu ◽  
Robert Talay ◽  
Zhengdong Xiao ◽  
David Rosenberg ◽  
...  

2013 ◽  
Vol 461 ◽  
pp. 565-569 ◽  
Author(s):  
Fang Wang ◽  
Kai Xu ◽  
Qiao Sheng Zhang ◽  
Yi Wen Wang ◽  
Xiao Xiang Zheng

Brain-machine interfaces (BMIs) decode cortical neural spikes of paralyzed patients to control external devices for the purpose of movement restoration. Neuroplasticity induced by conducting a relatively complex task within multistep, is helpful to performance improvements of BMI system. Reinforcement learning (RL) allows the BMI system to interact with the environment to learn the task adaptively without a teacher signal, which is more appropriate to the case for paralyzed patients. In this work, we proposed to apply Q(λ)-learning to multistep goal-directed tasks using users neural activity. Neural data were recorded from M1 of a monkey manipulating a joystick in a center-out task. Compared with a supervised learning approach, significant BMI control was achieved with correct directional decoding in 84.2% and 81% of the trials from naïve states. The results demonstrate that the BMI system was able to complete a task by interacting with the environment, indicating that RL-based methods have the potential to develop more natural BMI systems.


2009 ◽  
Vol 65 ◽  
pp. S49
Author(s):  
Kimiko Kawashima ◽  
Keiichiro Shindo ◽  
Junichi Ushiba ◽  
Yutaka Tomita ◽  
Yoshihisa Masakado ◽  
...  

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