A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
The problem of serial order: a neural network model of sequence learning and recall
Current research in natural language generation
On the Problem of Local Minima in Backpropagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Chaotic balanced state in a model of cortical circuits
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
Neural Networks - Special issue on the global brain: imaging and modelling
Hypersphere ART and ARTMAP for Unsupervised and Supervised, Incremental Learning
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Fast synchronization of perceptual grouping in laminar visual cortical circuits
Neural Networks - 2004 Special issue Vision and brain
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Soliciting customer requirements for product redesign based on picture sorts and ART2 neural network
Expert Systems with Applications: An International Journal
2007 Special Issue: Consciousness CLEARS the mind
Neural Networks
The application of clustering analysis for the critical areas on TFT-LCD panel
Expert Systems with Applications: An International Journal
Computers and Electronics in Agriculture
A self-organizing neural model of motor equivalent reaching and tool use by a multijoint arm
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
The hippocampus and cerebellum in adaptively timed learning, recognition, and movement
Journal of Cognitive Neuroscience
Cortical synchronization and perceptual framing
Journal of Cognitive Neuroscience
Ovarian cancer diagnosis with complementary learning fuzzy neural network
Artificial Intelligence in Medicine
Computers in Biology and Medicine
Discovery of hierarchical thematic structure in text collections with adaptive resonance theory
Neural Computing and Applications
Fault diagnosis of pneumatic systems with artificial neural network algorithms
Expert Systems with Applications: An International Journal
AG-ART: An adaptive approach to evolving ART architectures
Neurocomputing
Manufacturing cell formation with production data using neural networks
Computers and Industrial Engineering
dFasArt: Dynamic neural processing in FasArt model
Neural Networks
Electric load forecasting using a fuzzy ART&ARTMAP neural network
Applied Soft Computing
Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system
IEEE Transactions on Intelligent Transportation Systems
Journal of Cognitive Neuroscience
Cascade ARTMAP: integrating neural computation and symbolic knowledge processing
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A neural architecture for pattern sequence verification through inferencing
IEEE Transactions on Neural Networks
An optoelectronic implementation of the adaptive resonance neural network
IEEE Transactions on Neural Networks
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Adaptive Resonance Theory, or ART, is a cognitive and neural theory of how the brain autonomously learns to categorize, recognize, and predict objects and events in a changing world. This article reviews classical and recent developments of ART, and provides a synthesis of concepts, principles, mechanisms, architectures, and the interdisciplinary data bases that they have helped to explain and predict. The review illustrates that ART is currently the most highly developed cognitive and neural theory available, with the broadest explanatory and predictive range. Central to ART's predictive power is its ability to carry out fast, incremental, and stable unsupervised and supervised learning in response to a changing world. ART specifies mechanistic links between processes of consciousness, learning, expectation, attention, resonance, and synchrony during both unsupervised and supervised learning. ART provides functional and mechanistic explanations of such diverse topics as laminar cortical circuitry; invariant object and scenic gist learning and recognition; prototype, surface, and boundary attention; gamma and beta oscillations; learning of entorhinal grid cells and hippocampal place cells; computation of homologous spatial and temporal mechanisms in the entorhinal-hippocampal system; vigilance breakdowns during autism and medial temporal amnesia; cognitive-emotional interactions that focus attention on valued objects in an adaptively timed way; item-order-rank working memories and learned list chunks for the planning and control of sequences of linguistic, spatial, and motor information; conscious speech percepts that are influenced by future context; auditory streaming in noise during source segregation; and speaker normalization. Brain regions that are functionally described include visual and auditory neocortex; specific and nonspecific thalamic nuclei; inferotemporal, parietal, prefrontal, entorhinal, hippocampal, parahippocampal, perirhinal, and motor cortices; frontal eye fields; supplementary eye fields; amygdala; basal ganglia: cerebellum; and superior colliculus. Due to the complementary organization of the brain, ART does not describe many spatial and motor behaviors whose matching and learning laws differ from those of ART. ART algorithms for engineering and technology are listed, as are comparisons with other types of models.