Synonyms
Definition
Adaptive resonance theory, or ART, is both a cognitive and neural theory of how the brain quickly learns to categorize, recognize, and predict objects and events in a changing world, and a set of algorithms that computationally embody ART principles and that are used in large-scale engineering and technological applications wherein fast, stable, and incremental learning about complex changing environment is needed. ART clarifies the brain processes from which conscious experiences emerge. It predicts a functional link between processes of consciousness, learning, expectation, attention, resonance, and synchrony (CLEARS), including the prediction that “all conscious states are resonant states.” This connection clarifies how brain dynamics enable a behaving individual to autonomously adapt in real time to a rapidly changing world. ART predicts how top-down attention works and regulates fast stable learning of recognition categories. In particular, ART articulates...
This is a preview of subscription content, log in via an institution to check access.
Recommended Reading
Bullier, J., Hupé, J. M., James, A., & Girard, P. (1996). Functional interactions between areas V1 and V2 in the monkey. Journal of Physiology Paris, 90(3–4), 217–220.
Carpenter, G. A. (1997). Distributed learning, recognition, and prediction by ART and ARTMAP neural networks. Neural Networks, 10, 1473–1494.
Carpenter, G. A. & Gaddam, S. C. (2010). Biased ART: A neural architecture that shifts attention towards previously disregarded features following an incorrect prediction. Neural Networks, 23.
Carpenter, G. A., & Grossberg, S. (1987). A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing, 37, 54–115.
Carpenter, G. A. & Grossberg, S. (1993). Normal and amnesic learning, recognition, and memory by a neural model of cortico-hippocampal interactions. Trends in Neurosciences, 16, 131–137.
Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H. & Rosen, D. B. (1992). Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks, 3, 698–713.
Carpenter, G. A., Martens, S., & Ogas, O. J. (2005). Self-organizing information fusion and hierarchical knowledge discovery: A new framework using ARTMAP neural networks. Neural Networks, 18, 287–295.
Carpenter, G. A., Milenova, B. L., & Noeske, B. W. (1998). Distributed ARTMAP: A neural network for fast distributed supervised learning. Neural Networks, 11, 793–813.
Caudell, T. P., Smith, S. D. G., Escobedo, R., & Anderson, M. (1994). NIRS: Large scale ART 1 neural architectures for engineering design retrieval. Neural Networks, 7, 1339–1350.
Grossberg, S. (1976). Adaptive pattern classification and universal recoding, II: Feedback, expectation, olfaction, and illusions. Biological Cybernetics, 23, 187–202.
Grossberg, S. (1980). How does a brain build a cognitive code? Psychological Review, 87, 1–51.
Grossberg, S. (1999). The link between brain, learning, attention, and consciousness. Consciousness and Cognition, 8, 1–44.
Grossberg, S. (2000). The complementary brain: Unifying brain dynamics and modularity. Trends in Cognitive Sciences, 4, 233–246.
Grossberg, S. (2003). How does the cerebral cortex work? Development, learning, attention, and 3D vision by laminar circuits of visual cortex. Behavioral and Cognitive Neuroscience Reviews, 2, 47–76.
Grossberg, S. (2007). Consciousness CLEARS the mind. Neural Networks, 20, 1040–1053.
Grossberg, S. & Versace, M. (2008). Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Research, 1218, 278–312.
Parsons, O., & Carpenter, G. A. (2003). ARTMAP neural networks for information fusion and data mining: Map production and target recognition methodologies. Neural Networks, 16(7), 1075–1089.
Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.
Raizada, R., & Grossberg, S. (2003). Towards a theory of the laminar architecture of cerebral cortex: Computational clues from the visual system. Cerebral Cortex, 13, 100–113.
Sillito, A. M., Jones, H. E., Gerstein, G. L., & West, D. C. (1994). Feature-linked synchronization of thalamic relay cell firing induced by feedback from the visual cortex. Nature, 369, 479–482.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this entry
Cite this entry
Carpenter, G.A., Grossberg, S. (2011). Adaptive Resonance Theory. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_11
Download citation
DOI: https://doi.org/10.1007/978-0-387-30164-8_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering