Skip to main content
SpringerLink
Log in

Adaptive Resonance Theory

  • Reference work entry
  • First Online:
Book cover Encyclopedia of Machine Learning and Data Mining

Abstract

Computational models based on cognitive and neural systems are now deeply embedded in the standard repertoire of machine learning and data mining methods, with intelligent learning systems enhancing performance in nearly every existing application area. Beyond data mining, this article shows how models based on adaptive resonance theory (ART) may provide entirely new questions and practical solutions for technological applications. ART models carry out hypothesis testing, search, and incremental fast or slow, self-stabilizing learning, recognition, and prediction in response to large nonstationary databases (big data). Three computational examples, each based on the distributed ART neural network, frame questions and illustrate how a learning system (each with no free parameters) may enhance the analysis of large-scale data. Performance of each task is simulated on a common mapping platform, a remote sensing dataset called the Boston Testbed, available online along with open-source system code. Key design elements of ART models and links to software for each system are included. The article further points to future applications for integrative ART-based systems that have already been computationally specified and simulated. New application directions include autonomous robotics, general-purpose machine vision, audition, speech recognition, language acquisition, eye movement control, visual search, figure-ground separation, invariant object recognition, social cognition, object and spatial attention, scene understanding, space-time integration, episodic memory, navigation, object tracking, system-level analysis of mental disorders, and machine consciousness.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 699.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 949.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  • Amis GP, Carpenter GA (2010) Self-supervised ARTMAP. Neural Netw 23:265–282

    Article  Google Scholar 

  • Baloch AA, Grossberg S (1997) A neural model of high-level motion processing: line motion and formotion dynamics. Vis Res 37:3037–3059

    Article  Google Scholar 

  • Baloch AA, Grossberg S, Mingolla E, Nogueira CAM (1999) A neural model of first-order and second-order motion perception and magnocellular dynamics. J Opt Soc Am A 16:953–978

    Article  Google Scholar 

  • Berzhanskaya J, Grossberg S, Mingolla E (2007) Laminar cortical dynamics of visual form and motion interactions during coherent object motion perception. Spat Vis 20:337–395

    Article  Google Scholar 

  • Brown J, Bullock D, Grossberg S (1999) How the basal ganglia use parallel excitatory and inhibitory learning pathways to selectively respond to unexpected rewarding cues. J Neurosci 19:10502–10511

    Google Scholar 

  • Brown JW, Bullock D, Grossberg S (2004) How laminar frontal cortex and basal ganglia circuits interact to control planned and reactive saccades. Neural Netw 17:471–510

    Article  MATH  Google Scholar 

  • Browning A, Grossberg S, Mingolla M (2009a) A neural model of how the brain computes heading from optic flow in realistic scenes. Cogn Psychol 59:320–356

    Article  Google Scholar 

  • Browning A, Grossberg S, Mingolla M (2009b) Cortical dynamics of navigation and steering in natural scenes: motion-based object segmentation, heading, and obstacle avoidance. Neural Netw 22:1383–1398

    Article  Google Scholar 

  • Bullock D, Grossberg S (1988) Neural dynamics of planned arm movements: emergent invariants and speed-accuracy properties during trajectory formation. Psychol Rev 95:49–90

    Article  Google Scholar 

  • Bullock D, Grossberg S (1991) Adaptive neural networks for control of movement trajectories invariant under speed and force rescaling. Hum Mov Sci 10:3–53

    Article  Google Scholar 

  • Bullock D, Cisek P, Grossberg S (1998) Cortical networks for control of voluntary arm movements under variable force conditions. Cereb Cortex 8:48–62

    Article  Google Scholar 

  • Cao Y, Grossberg S (2005) A laminar cortical model of stereopsis and 3D surface perception: closure and da Vinci stereopsis. Spat Vis 18:515–578

    Article  Google Scholar 

  • Cao Y, Grossberg S (2012) Stereopsis and 3D surface perception by spiking neurons in laminar cortical circuits: a method of converting neural rate models into spiking models. Neural Netw 26:75–98

    Article  Google Scholar 

  • Cao Y, Grossberg S, Markowitz J (2011) How does the brain rapidly learn and reorganize view- and positionally-invariant object representations in inferior temporal cortex? Neural Netw 24:1050–1061

    Article  MATH  Google Scholar 

  • Carpenter GA (1994) A distributed outstar network for spatial pattern learning. Neural Netw 7:159–168

    Article  Google Scholar 

  • Carpenter GA (1997) Distributed learning, recognition, and prediction by ART and ARTMAP neural networks. Neural Netw 10:1473–1494

    Article  Google Scholar 

  • Carpenter GA (2001) Neural network models of learning and memory: leading questions and an emerging framework. Trends Cogn Sci 5:114–118

    Article  Google Scholar 

  • Carpenter GA, Gaddam SC (2010) Biased ART: a neural architecture that shifts attention toward previously disregarded features following an incorrect prediction. Neural Netw 23:435–451

    Article  Google Scholar 

  • Carpenter GA, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis Graph Image Process 37:54–115

    Article  MATH  Google Scholar 

  • Carpenter GA, Grossberg S (1990) ART 3: hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Netw 4: 129–152

    Article  Google Scholar 

  • Carpenter G, Grossberg S (1993) Normal and amnesic learning, recognition, and memory by a neural model of cortico-hippocampal interactions. Trends Neurosci 16:131–137

    Article  Google Scholar 

  • Carpenter GA, Markuzon N (1998) ARTMAP-IC and medical diagnosis: instance counting and inconsistent cases. Neural Netw 11:323–336

    Article  Google Scholar 

  • Carpenter GA, Grossberg S, Reynolds JH (1991a) ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Netw 4:565–588

    Article  Google Scholar 

  • Carpenter GA, Grossberg S, Rosen DB (1991b) Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4:759–771

    Article  Google Scholar 

  • Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB (1992) Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3:698–713

    Article  Google Scholar 

  • Carpenter GA, Martens S, Ogas OJ (2005) Self-organizing information fusion and hierarchical knowledge discovery: a new framework using ARTMAP neural networks. Neural Netw 18:287–295

    Article  Google Scholar 

  • Chandler B, Grossberg S (2012) Joining distributed pattern processing and homeostatic plasticity in recurrent on-center off-surround shunting networks: noise, saturation, short-term memory, synaptic scaling, and BDNF. Neural Netw 25: 21–29

    Article  Google Scholar 

  • Chang H-C, Grossberg S, Cao Y (2014) Where’s Waldo? How perceptual cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene. Front Integr Neurosci doi:10.3389/fnint.2014.0043

    Google Scholar 

  • Chapelle O, Schölkopf B, Zien A (eds) (2006) Semi-supervised learning. MIT, Cambridge

    Google Scholar 

  • Contreras-Vidal JL, Grossberg S, Bullock D (1997) A neural model of cerebellar learning for arm movement control: cortico-spino-cerebellar dynamics. Learn Mem 3:475–502

    Article  Google Scholar 

  • Dranias M, Grossberg S, Bullock D (2008) Dopaminergic and non-dopaminergic value systems in conditioning and outcome-specific revaluation. Brain Res 1238:239–287

    Article  Google Scholar 

  • Elder D, Grossberg S, Mingolla M (2009) A neural model of visually guided steering, obstacle avoidance, and route selection. J Exp Psychol Hum Percept Perform 35:1501–1531

    Article  Google Scholar 

  • Fang L, Grossberg S (2009) From stereogram to surface: how the brain sees the world in depth. Spat Vis 22:45–82

    Article  Google Scholar 

  • Fazl A, Grossberg S, Mingolla E (2009) View-invariant object category learning, recognition, and search: how spatial and object attention are coordinated using surface-based attentional shrouds. Cogn Psychol 58:1–48

    Article  Google Scholar 

  • Foley NC, Grossberg S, Mingolla E (2012) Neural dynamics of object-based multifocal visual spatial attention and priming: object cueing, useful-field-of-view, and crowding. Cognitive Psychology 65:77–117

    Article  Google Scholar 

  • Gancarz G, Grossberg G (1999) A neural model of the saccadic eye movement control explains task-specific adaptation. Vis Res 39:3123–3143

    Article  Google Scholar 

  • Grossberg S (1984) Some psychophysiological and pharmacological correlates of a developmental, cognitive, and motivational theory. In: Karrer R, Cohen J, Tueting P (eds) Brain and information: event related potential. New York Academy of Sciences, New York, pp 58–142.

    Google Scholar 

  • Grossberg S (2000a) The complementary brain: unifying brain dynamics and modularity. Trends Cogn Sci 4:233–246

    Article  Google Scholar 

  • Grossberg S (2000b) The imbalanced brain: from normal behavior to schizophrenia. Biol Psychiatry 48:81–98

    Article  Google Scholar 

  • Grossberg S (2013) Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing World. Neural Netw 37:1–47

    Article  Google Scholar 

  • Grossberg, S. (2016). Towards solving the hard problem of consciousness: the varieties of brain resonances and the conscious experiences that they support. Submitted for publication

    Google Scholar 

  • Grossberg S, Huang T-R (2009) ARTSCENE: a neural system for natural scene classification. J Vis 9(6):1–19

    Article  Google Scholar 

  • Grossberg S, Merrill JWL (1992) A neural network model of adaptively timed reinforcement learning and hippocampal dynamics. Cogn Brain Res 1:3–38

    Article  Google Scholar 

  • Grossberg S, Merrill JWL (1996) The hippocampus and cerebellum in adaptively timed learning, recognition, and movement. J Cogn Neurosci 8:257–277

    Article  Google Scholar 

  • Grossberg S, Pearson L (2008) Laminar cortical dynamics of cognitive and motor working memory, sequence learning and performance: toward a unified theory of how the cerebral cortex works. Psychol Rev 115:677–732

    Article  Google Scholar 

  • Grossberg S, Pilly PK (2012) How entorhinal grid cells may learn multiple spatial scales from a dorsoventral gradient of cell response rates in a self-organizing map. PLoS Comput Biol 8(10):31002648. doi: 10.1371/journal.pcbi.1002648

    Article  MathSciNet  Google Scholar 

  • Grossberg S, Pilly PK (2014) Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention, and oscillations. Philos Trans R Soc B 369:20120524

    Article  Google Scholar 

  • Grossberg S, Rudd ME (1992) Cortical dynamics of visual motion perception: short-range and long-range apparent motion (with Rudd ME). Psychol Rev 99:78–121

    Article  Google Scholar 

  • Grossberg S, Schmajuk NA (1989) Neural dynamics of adaptive timing and temporal discrimination during associative learning. Neural Netw 2:79–102

    Article  Google Scholar 

  • Grossberg S, Seidman D (2006) Neural dynamics of autistic behaviors: cognitive, emotional, and timing substrates. Psychol Rev 113:483–525

    Article  Google Scholar 

  • Grossberg S, Swaminathan G (2004) A laminar cortical model for 3D perception of slanted and curved surfaces and of 2D images: development, attention and bistability. Vis Res 44:1147–1187

    Article  Google Scholar 

  • Grossberg S, Vladusich T (2010) How do children learn to follow gaze, share joint attention, imitate their teachers, and use tools during social interactions? Neural Netw 23:940–965

    Article  Google Scholar 

  • Grossberg S, Leveille J, Versace M (2011) How do object reference frames and motion vector decomposition emerge in laminar cortical circuits? Atten Percept Psychophys 73:1147–1170

    Article  Google Scholar 

  • Grossberg S, Kuhlmann L, Mingolla E (2007) A neural model of 3D shape-from-texture: multiple-scale filtering, boundary grouping, and surface filling-in. Vis Res 47:634–672

    Article  Google Scholar 

  • Grossberg S, Mingolla E, Viswanathan L (2001) Neural dynamics of motion integration and segmentation within and across apertures. Vis Res 41:2521–2553

    Article  Google Scholar 

  • Grossberg S, Srihasam K, Bullock D (2012) Neural dynamics of saccadic and smooth pursuit eye movement coordination during visual tracking of unpredictably moving targets. Neural Netw 27:1–20

    Article  Google Scholar 

  • Huang T-R, Grossberg S (2010) Cortical dynamics of contextually cued attentive visual learning and search: spatial and object evidence accumulation. Psychol Rev 117:1080–1112

    Article  Google Scholar 

  • Hurvich LM, Jameson D (1957) An opponent-process theory of color vision. Psychol Rev 64: 384–390

    Article  Google Scholar 

  • Kelly FJ, Grossberg S (2000) Neural dynamics of 3-D surface perception: figure-ground separation and lightness perception. Percept Psychophys 62:1596–1619

    Article  Google Scholar 

  • Mhatre H, Gorchetchnikov A, Grossberg S (2012) Grid cell hexagonal patterns formed by fast self-organized learning within entorhinal cortex. Hippocampus 22:320–334

    Article  Google Scholar 

  • Pack C, Grossberg S, Mingolla E (2001) A neural model of smooth pursuit control and motion perception by cortical area MST. J Cogn Neurosci 13:102–120

    Article  Google Scholar 

  • Pilly PK, Grossberg S (2012) How do spatial learning and memory occur in the brain? Coordinated learning of entorhinal grid cells and hippocampal place cells. J Cogn Neurosci 24:1031–1054

    Article  Google Scholar 

  • Pilly PK, Grossberg S (2014) How does the modular organization of entorhinal grid cells develop? Front Hum Neurosci. doi: 10.3389/fnhum.2014.0037

    Google Scholar 

  • Schiller PH (1982) Central connections of the retinal ON and OFF pathways. Nature 297:580–583

    Article  Google Scholar 

  • Simone G, Farina A, Morabito FC, Serpico SB, Bruzzone L (2002) Image fusion techniques for remote sensing applications. Inf Fusion 3:3–15

    Article  Google Scholar 

  • Srihasam K, Bullock D, Grossberg S (2009) Target selection by frontal cortex during coordinated saccadic and smooth pursuit eye movements. J Cogn Neurosci 21:1611–1627

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics & Center for Adaptive Systems, Boston University, 677 Beacon Street, 02215, Boston, MA, USA

    Gail A. Carpenter

  2. Center for Adaptive Systems, Graduate Program in Cognitive and Neural Systems, Department of Mathematics, Boston University, 677 Beacon Street, 02215, Boston, MA, USA

    Stephen Grossberg

Authors
  1. Gail A. Carpenter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen Grossberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gail A. Carpenter .

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this entry

Cite this entry

Carpenter, G.A., Grossberg, S. (2017). Adaptive Resonance Theory. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_6

Download citation

Publish with us

Policies and ethics

Search

Navigation