SOAR: an architecture for general intelligence
Artificial Intelligence
Unified theories of cognition
Cambrian intelligence: the early history of the new AI
Cambrian intelligence: the early history of the new AI
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Communications of the ACM
Multiple model-based reinforcement learning
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
The gamma-filter-a new class of adaptive IIR filters withrestricted feedback
IEEE Transactions on Signal Processing
From neural networks to the brain: autonomous mental development
IEEE Computational Intelligence Magazine
Spatial-temporal clustering of neural data using linked-mixtures of hidden Markov models
EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Hi-index | 0.00 |
The success of brain-machine interfaces (BMI) is enabled by the remarkable ability of the brain to incorporate the artificial neuroprosthetic 'tool' into its own cognitive space and use it as an extension of the user's body. Unlike other tools, neuroprosthetics create a shared space that seamlessly spans the user's internal goal representation of the world and the external physical environment enabling a much deeper human-tool symbiosis. A key factor in the transformation of 'simple tools' into 'intelligent tools' is the concept of co-adaptation where the tool becomes functionally involved in the extraction and definition of the user's goals. Recent advancements in the neuroscience and engineering of neuroprosthetics 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 conventional methodologies. By designing adaptive controls and artificial intelligence into the neural interface, tools can become active assistants in goal-directed behavior and further enhance human performance in particular for the disabled population. This paper presents recent advances in computational and neural systems supporting the development of symbiotic neuroprosthetic assistants.