The ART of adaptive pattern recognition by a self-organizing neural network
1988/03/01 by G.A. Carpenter, Gail A. Carpenter, S. Grossberg +1 · 1,294 citations
Computer Science · Neuroscience · #Adaptive resonance theory #Adaptive system #Architecture #Artificial intelligence #Artificial neural network #Cognition #Cognitive Science and Education Research #Computer science #Machine learning #Neural Networks and Applications #Neural dynamics and brain function #Process (computing) #Property (philosophy) #Stability (learning theory)
paper · doi:10.1109/2.33
published in Computer 21(3), 77-88 (IEEE Computer Society)
openalex publication_date 1988/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Abstract
The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to significant events and yet remain stable in response to irrelevant events. ART architectures are discussed that are neural networks that self-organize stable recognition codes in real time in response to arbitrary sequences of input patterns. Within such an ART architecture, the process of adaptive pattern recognition is a special case of the more general cognitive process of hypothesis discovery, testing, search, classification, and learning. This property opens up the possibility of applying ART systems to more general problems of adaptively processing large abstract information sources and databases. The main computational properties of these ART architectures are outlined and contrasted with those of alternative learning and recognition systems.>
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