Prerequesites for symbiotic brain-machine interfaces

Authors:
Justin C. Sanchez;Jose C. Principe
Affiliations:
Depts. of Pediatrics, Neuroscience, and Biomedical Engineering, University of Florida, Gainesville;Department of Electrical and Computer Engineering, University of Florida, Gainesville
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

Citing 11
Cited 0

Cambrian intelligence: the early history of the new AI

Cambrian intelligence: the early history of the new AI
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Adaptive brain interfaces

Communications of the ACM
Multiple model-based reinforcement learning

Neural Computation
Towards an adaptive man-machine interface for virtual environments

IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines

Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Brain-Based Devices for the Study of Nervous Systems and the Development of Intelligent Machines

Artificial Life
Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms

Neural Computation
Intentional dynamic systems: Fundamental concepts and applications: Introduction

International Journal of Intelligent Systems - Intentional Dynamic Systems—Foundations, Modeling, and Robot Implementation
The misbehavior of value and the discipline of the will

Neural Networks - 2006 Special issue: Neurobiology of decision making
2009 Special Issue: Coordinated machine learning and decision support for situation awareness

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent advancements in the neuroscience and engineering of Brain-Machine Interfaces are providing a blueprint for how new co-adaptive designs based on reinforcement learning change the nature of a user's ability to accomplish tasks that were not possible using static methodologies. By designing adaptive controls and artificial intelligence into the neural interface, computers can become active assistants in goal-directed behavior and further enhance human performance. This paper presents a set of minimal prerequisites that enable a cooperative symbiosis and dialogue between biological and artificial systems.