Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Adaptive Resonance Theory

  • Gail A. Carpenter
  • Stephen Grossberg
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_11




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 to check access.

Recommended Reading

  1. 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.Google Scholar
  2. Carpenter, G. A. (1997). Distributed learning, recognition, and prediction by ART and ARTMAP neural networks. Neural Networks, 10, 1473–1494.Google Scholar
  3. 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.Google Scholar
  4. 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.Google Scholar
  5. 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.Google Scholar
  6. 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.Google Scholar
  7. 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.Google Scholar
  8. 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.Google Scholar
  9. 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.Google Scholar
  10. Grossberg, S. (1976). Adaptive pattern classification and universal recoding, II: Feedback, expectation, olfaction, and illusions. Biological Cybernetics, 23, 187–202.zbMATHMathSciNetGoogle Scholar
  11. Grossberg, S. (1980). How does a brain build a cognitive code? Psychological Review, 87, 1–51.Google Scholar
  12. Grossberg, S. (1999). The link between brain, learning, attention, and consciousness. Consciousness and Cognition, 8, 1–44.Google Scholar
  13. Grossberg, S. (2000). The complementary brain: Unifying brain dynamics and modularity. Trends in Cognitive Sciences, 4, 233–246.Google Scholar
  14. 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.Google Scholar
  15. Grossberg, S. (2007). Consciousness CLEARS the mind. Neural Networks, 20, 1040–1053.Google Scholar
  16. Grossberg, S. & Versace, M. (2008). Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Research, 1218, 278–312.Google Scholar
  17. 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.Google Scholar
  18. Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.Google Scholar
  19. 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.Google Scholar
  20. 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.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Gail A. Carpenter
  • Stephen Grossberg

There are no affiliations available