Literature DB >> 23149242

Adaptive Resonance Theory: how a brain learns to consciously attend, learn, and recognize a changing world.

Stephen Grossberg1.   

Abstract

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.
Copyright © 2012 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2012        PMID: 23149242     DOI: 10.1016/j.neunet.2012.09.017

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  58 in total

Review 1.  Learning to promote recovery after spinal cord injury.

Authors:  James W Grau; Rachel E Baine; Paris A Bean; Jacob A Davis; Gizelle N Fauss; Melissa K Henwood; Kelsey E Hudson; David T Johnston; Megan M Tarbet; Misty M Strain
Journal:  Exp Neurol       Date:  2020-04-28       Impact factor: 5.330

Review 2.  How neuroscience can inform the study of individual differences in cognitive abilities.

Authors:  Dennis J McFarland
Journal:  Rev Neurosci       Date:  2017-05-24       Impact factor: 4.353

3.  Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements.

Authors:  Stephen Grossberg; Karthik Srinivasan; Arash Yazdanbakhsh
Journal:  Front Psychol       Date:  2015-01-14

4.  Where's Waldo? How perceptual, cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene.

Authors:  Hung-Cheng Chang; Stephen Grossberg; Yongqiang Cao
Journal:  Front Integr Neurosci       Date:  2014-06-17

5.  Oscillatory dynamics of cortical functional connections in semantic prediction.

Authors:  Fahimeh Mamashli; Sheraz Khan; Jonas Obleser; Angela D Friederici; Burkhard Maess
Journal:  Hum Brain Mapp       Date:  2018-12-07       Impact factor: 5.038

6.  Neural Mechanisms of Human Decision-Making.

Authors:  Seth Herd; Kai Krueger; Ananta Nair; Jessica Mollick; Randall O'Reilly
Journal:  Cogn Affect Behav Neurosci       Date:  2021-01-06       Impact factor: 3.282

7.  Toward an Integration of Deep Learning and Neuroscience.

Authors:  Adam H Marblestone; Greg Wayne; Konrad P Kording
Journal:  Front Comput Neurosci       Date:  2016-09-14       Impact factor: 2.380

Review 8.  How the prefrontal executive got its stripes.

Authors:  Helen Barbas; Miguel Ángel García-Cabezas
Journal:  Curr Opin Neurobiol       Date:  2016-07-29       Impact factor: 6.627

Review 9.  Steady-state visual evoked potentials as a research tool in social affective neuroscience.

Authors:  Matthias J Wieser; Vladimir Miskovic; Andreas Keil
Journal:  Psychophysiology       Date:  2016-10-04       Impact factor: 4.016

10.  Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations.

Authors:  Stephen Grossberg; Praveen K Pilly
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2013-12-23       Impact factor: 6.237

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.