Learning to Attend, Recognize, and Predict the World

Author(s):  
Stephen Grossberg

This chapter begins to explain many of our most important perceptual and cognitive abilities, including how we rapidly learn to categorize and recognize so many objects and events in the world, how we remember and anticipate events that may occur in familiar situations, how we pay attention to events that particularly interest us, and how we become conscious of these events. These abilities enable us to engage in fantasy activities such as visual imagery, internalized speech, and planning. They support our ability to learn language quickly and to complete and consciously hear speech sounds in noise. The chapter begins to explain key differences between perception and recognition, and introduces Adaptive Resonance Theory, or ART, which is now the most advanced cognitive and neural theory of how our brains learn to attend, recognize, and predict objects and events in a changing world. ART cycles of resonance and reset solve the stability-plasticity dilemma so that we can learn quickly without new learning forcing catastrophic forgetting of previously learned memories. ART can learn quickly or slowly, with supervision and without it, and both many-to-one maps and one-to-many maps. It uses learned top-down expectations, attentional focusing, and mismatch-mediated hypothesis testing to do so, and is thus a self-organizing production system. ART can be derived from a simple thought experiment, and explains and predicts many psychological and neurobiological data about normal behavior. When these processes break down in specific ways, they cause symptoms of mental disorders such as schizophrenia, autism, amnesia, and Alzheimer’s disease.

2009 ◽  
Vol 147-149 ◽  
pp. 74-79 ◽  
Author(s):  
Petar Ćurković ◽  
Bojan Jerbić ◽  
Tomislav Stipančić

In this paper, an integration of Honey bees mating algorithm (HBMA) and adaptive resonance theory neural network (ART1) for efficient path planning of a mobile robot in a static environment is presented. The robot must find shortest route from given origin to the target position. Moreover, it should be able to memorize the environment and, if it faces known world, execute already learned trajectory found by HBMA solver, or solve the world and memorize the trajectory for the given environment. This is done using Adaptive Resonance Theory based neural network. This way simulated robot is able to navigate through environment and to continuously increase its knowledge.


2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.


1992 ◽  
Vol 03 (01) ◽  
pp. 57-63 ◽  
Author(s):  
Eamon P. Fulcher

WIS-ART merges the self-organising properties of Adaptive Resonance Theory (ART) with the operation of WISARD, an adaptive pattern recognition machine which uses discriminators of conventional Random Access Memories (RAMs). The result is an unsupervised pattern clustering system operating at near real-time that implements the leader algorithm. ART’s clustering is highly dependent upon the value of a “vigilance” parameter, which is set prior to training. However, for WIS-ART hierarchical clustering is performed automatically by the partitioning of discriminators into “multi-vigilance modules”. Thus, clustering may be controlled during the test phase according to the degree of discrimination (hierarchical level) required. Methods for improving the clustering characteristics of WIS-ART whilst still retaining stability are discussed.


2017 ◽  
Vol 50 (6) ◽  
pp. 430-438 ◽  
Author(s):  
Yoshinari Hori ◽  
Hiroki Yamamoto ◽  
Tomoko Suzuki ◽  
Jun Okitsu ◽  
Tomohiro Nakamura ◽  
...  

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