title: From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.
abstract: This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory.
authors.affiliation: Center for Adaptive Systems, Graduate Program in Cognitive and Neural Systems, Center for Computational Neuroscience and Neural Technology, Department of Mathematics, Boston University, Boston, MA 02215, United States. Electronic address: http://cns.bu.edu/~steve.
authors.forename: Stephen
authors.initials: S
authors.lastname: Grossberg
mesh_terms: D000818:Animals; D001288:Attention / Q000502:physiology; D001921:Brain / Q000502:physiology*; D003243:Consciousness / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009043:Motor Activity; D009473:Neuronal Plasticity; D021641:Recognition, Psychology / Q000502:physiology; D065854:Spatial Navigation / Q000502:physiology; D013569:Synapses / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology
keywords: 3D vision; Adaptive resonance theory; Adaptively controlled conditioning; Attention; Autism; Category learning; Cognitive working memory; Eye movement; Grid cell; Laminar cortical circuits; Learning; Medial temporal amnesia; Memory; Place cell; Predictive remapping; Spatial navigation; Speech perception; Time cell; mGluR