Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions

By Stephen Grossberg. 1976

Subfields: Representation & Self-Supervised Learning, Statistical Learning Theory

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, methodology): “Parallel to these developments, a radically different approach was being explored based on neuronlike “sub-symbolic” computations (e.g., Fukushima 1980; Grossberg 1976; Rosenblatt 1958).”
Deep learning in neural networks: An overview (2014, neutral): “…1992; Baldi & Hornik, 1989; Barlow, Kaushal, & Mitchison, 1989; Barrow, 1987; Deco & Parra, 1997; Field, 1987; Földiák, 1990; Földiák & Young, 1995; Grossberg, 1976a, 1976b; Hebb, 1949; Kohonen, 1972, 1982, 1988; Kosko, 1990; Martinetz, Ritter, & Schulten, 1990; Miller, 1994; Mozer, 1991; Oja,…”
Adaptive resonance theory (1997, background): “…an analysis of experimental literatures in VISIOn, speech, cortical development, and reinforcement learning, including attentional blocking and cognitive-emotional interactions, led to the introduction of adaptive resonance as a theory of human cognitive information processing (Grossberg, 1976).”
Pattern analysis for machine olfaction: a review (2002, background): “Adaptive resonance theory (ART) [108], in particular, provides a mechanism that solves the “stability-plasticity dilemma,” which refers to the inability of most learning systems to adapt to changing environments”
Neural representations and mechanisms for the performance of simple speech sequences (2010, unknown): “Learning rules that conserve total synaptic strength have been proposed elsewhere (Grossberg, 1976; von der Malsburg, 1973), and similar conservational principles have been observed empirically (Markram & Tsodyks, 1996).”
Modeling attention to salient proto-objects (2006, unknown): “The idea of using mutually inhibiting networks of model neurons for this purpose goes back, at least, to Grossberg (1976a, 1976b) .”
2005 Special Issue (2005, unknown): “The successful learning rule for spiking neurons can sample some function q of this product (Grossberg, 1974; 1976a,b) or postsyn-aptically gated decay Grossberg instar (Grossberg, 1974; 1976a,b) in order to encode both spatial and temporal components of the pattern.”
Learning under Concept Drift: an Overview (2010, background): “Adaptive resonance theory, dating back 30 years [60], is based on the model of information processing by the brain [25].”
A neural theory of visual attention: bridging cognition and neurophysiology. (2005, methodology): “A winner-take-all (WTA) cluster of a general type proposed by Grossberg (1976, 1980) can be used.”
Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world (2013, methodology): “The search process exploits a type of medium-term memory acting at category cells that is realized by activity-dependent habituative transmitter gates (Carpenter & Grossberg, 1990; Grossberg, 1968c, 1972b, 1976b).”
Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. (2003, methodology): “The algorithm we used incorporates several widely accepted characteristics of neural computation, including Hebbian long-term potentiation/long-term depression (LTP/LTD) and inhibitory competition between neurons, that were first brought together by Grossberg (1976). (For more information on these m”