國立虎尾科技大學 |

Integrating robotic action with biologic perception: A brain-machine symbiosis theory.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Integrating robotic action with biologic perception: A brain-machine symbiosis theory./
Author:	Mahmoudi, Babak.
Description:	135 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-10, Section: B, page: 6159.
Contained By:	Dissertation Abstracts International72-10B.
Subject:	Engineering, Biomedical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3467684
ISBN:	9781124796420

Integrating robotic action with biologic perception: A brain-machine symbiosis theory.
Mahmoudi, Babak.

Integrating robotic action with biologic perception: A brain-machine symbiosis theory. - 135 p.

Source: Dissertation Abstracts International, Volume: 72-10, Section: B, page: 6159.

Thesis (Ph.D.)--University of Florida, 2010.

In patients with motor disability the natural cyclic flow of information between the brain and external environment is disrupted by their limb impairment. Brain-Machine Interfaces (BMIs) aim to provide new communication channels between the brain and environment by direct translation of brain's internal states into actions. For enabling the user in a wide range of daily life activities, the challenge is designing neural decoders that autonomously adapt to different tasks, environments, and to changes in the pattern of neural activity. In this dissertation, a novel decoding framework for BMIs is developed in which a computational agent autonomously learns how to translate neural states into action based on maximization of a measure of shared goal between user and the agent. Since the agent and brain share the same goal, a symbiotic relationship between them will evolve therefore this decoding paradigm is called a Brain-Machine Symbiosis (BMS) framework. A decoding agent was implemented within the BMS framework based on the Actor-Critic method of Reinforcement Learning. The rule of the Actor as a neural decoder was to find mapping between the neural representation of motor states in the primary motor cortex (MI) and robot actions in order to solve reaching tasks. The Actor learned the optimal control policy using an evaluative feedback that was estimated by the Critic directly from the user's neural activity of the Nucleus Accumbens (NAcc). Through a series of computational neuroscience studies in a cohort of rats it was demonstrated that NAcc could provide a useful evaluative feedback by predicting the increase or decrease in the probability of earning reward based on the environmental conditions. Using a closed-loop BMI simulator it was demonstrated the Actor-Critic decoding architecture was able to adapt to different tasks as well as changes in the pattern of neural activity. The custom design of a dual micro-wire array enabled simultaneous implantation of MI and NAcc for the development of a full closed-loop system. The Actor-Critic decoding architecture was able to solve the brain-controlled reaching task using a robotic arm by capturing the interdependency between the simultaneous action representation in MI and reward expectation in NAcc.

ISBN: 9781124796420Subjects--Topical Terms:

845403
Engineering, Biomedical.

Integrating robotic action with biologic perception: A brain-machine symbiosis theory.
LDR:03205nam 2200289 4500 001 712936
005 20121003100304.5
008 121101s2010 ||||||||||||||||| ||eng d
020 $a 9781124796420
035 $a (UMI)AAI3467684
035 $a AAI3467684
040 $a UMI $c UMI
100 1 $a Mahmoudi, Babak. $3 845431
245 1 0 $a Integrating robotic action with biologic perception: A brain-machine symbiosis theory.
300 $a 135 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-10, Section: B, page: 6159.
500 $a Adviser: Justin C. Sanchez.
502 $a Thesis (Ph.D.)--University of Florida, 2010.
520 $a In patients with motor disability the natural cyclic flow of information between the brain and external environment is disrupted by their limb impairment. Brain-Machine Interfaces (BMIs) aim to provide new communication channels between the brain and environment by direct translation of brain's internal states into actions. For enabling the user in a wide range of daily life activities, the challenge is designing neural decoders that autonomously adapt to different tasks, environments, and to changes in the pattern of neural activity. In this dissertation, a novel decoding framework for BMIs is developed in which a computational agent autonomously learns how to translate neural states into action based on maximization of a measure of shared goal between user and the agent. Since the agent and brain share the same goal, a symbiotic relationship between them will evolve therefore this decoding paradigm is called a Brain-Machine Symbiosis (BMS) framework. A decoding agent was implemented within the BMS framework based on the Actor-Critic method of Reinforcement Learning. The rule of the Actor as a neural decoder was to find mapping between the neural representation of motor states in the primary motor cortex (MI) and robot actions in order to solve reaching tasks. The Actor learned the optimal control policy using an evaluative feedback that was estimated by the Critic directly from the user's neural activity of the Nucleus Accumbens (NAcc). Through a series of computational neuroscience studies in a cohort of rats it was demonstrated that NAcc could provide a useful evaluative feedback by predicting the increase or decrease in the probability of earning reward based on the environmental conditions. Using a closed-loop BMI simulator it was demonstrated the Actor-Critic decoding architecture was able to adapt to different tasks as well as changes in the pattern of neural activity. The custom design of a dual micro-wire array enabled simultaneous implantation of MI and NAcc for the development of a full closed-loop system. The Actor-Critic decoding architecture was able to solve the brain-controlled reaching task using a robotic arm by capturing the interdependency between the simultaneous action representation in MI and reward expectation in NAcc.
590 $a School code: 0070.
650 4 $a Engineering, Biomedical. $3 845403
650 4 $a Nanoscience. $3 632473
650 4 $a Artificial Intelligence. $3 646849
690 $a 0541
690 $a 0565
690 $a 0800
710 2 $a University of Florida. $3 845432
773 0 $t Dissertation Abstracts International $g 72-10B.
790 1 0 $a Sanchez, Justin C., $e advisor
790 $a 0070
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3467684