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

By Stephen Grossberg. 2013

214 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

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Citations in the corpus

Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization (2018, background): “…similarities can be found between our model and the adaptive resonance theory (ART) in which neurons are iteratively adapted to a non-stationary input distribution in an unsupervised fashion and new neurons can be added in correspondence of dissimilar input (see Grossberg, 2012 for a review).”
FOSTER: Feature Boosting and Compression for Class-Incremental Learning (2022, background): “However, these methods with a single backbone may not have enough plasticity [17] to cope with the coming new categories.”
Adaptive resonance theory (1997, background): “How do specializations of this shared laminar design embody different types of biological intelligence, including vision, speech, language, and cognition? How does this shared design enable seamless intercortical interactions? Models of Laminar Computing clarify how these different types of intellig”
Continual Learning with Lifelong Vision Transformer (2022, background): “Continual learning requires neural networks to be stable to prevent forgetting, but also plastic to learn new streaming labels, which is referred to as the stability-plasticity dilemma [27, 53].”
Correlations of neural predictability and information transfer in cortex and their relation to predictive coding (2025, unknown): “However, feedforward connections could also convey neural activity patterns that are in agreement with the predictions of the generative model [6].”