title: Neuronal tuning in a brain-machine interface during Reinforcement Learning.
abstract: In this research, we have used neural tuning to quantify the neural representation of prosthetic arm's actions in a new framework of BMI, which is based on Reinforcement Learning (RLBMI). We observed that through closed-loop brain control, the neural representation has changed to encode robot actions that maximize rewards. This is an interesting result because in our paradigm robot actions are directly controlled by a Computer Agent (CA) with reward states compatible with the user's rewards. Through co-adaptation, neural modulation is used to establish the value of robot actions to achieve reward.
authors.affiliation: Department of Biomedical Engineering, University of Florida, 106 BME Building, Gainesville, 32611 USA. babakm@ufl.edu
authors.forename: Babak
authors.initials: B
authors.lastname: Mahmoudi
authors.forename: Jack
authors.initials: J
authors.lastname: Digiovanna
authors.forename: Jose C
authors.initials: JC
authors.lastname: Principe
authors.forename: Justin C
authors.initials: JC
authors.lastname: Sanchez
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003201:Computers; D004867:Equipment Design; D005246:Feedback; D007858:Learning; D008297:Male; D016571:Neural Networks, Computer; D051381:Rats; D017207:Rats, Sprague-Dawley; D012054:Reinforcement, Psychology; D012371:Robotics; D013997:Time Factors; D014584:User-Computer Interface