Grossberg Stephen
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.
Brain Res. 2015 Sep 24;1621:270-93. doi: 10.1016/j.brainres.2014.11.018. Epub 2014 Nov 20.
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.
本文概述了突触学习和记忆的神经模型，其在适应性行为中的表达关键取决于突触所嵌入的神经回路和系统。文章回顾了自适应共振理论（ART）模型，该模型利用兴奋性匹配和基于匹配的学习来实现快速分类学习，其学习到的记忆通过自上而下的期望、注意力聚焦和记忆搜索动态稳定。ART阐明了意识、学习、期望、注意力、共振和同步之间的机制关系。ART模型嵌入在ARTSCAN架构中，该架构统一了不变物体类别学习、识别、空间和物体注意力、预测性重映射以及眼动搜索等过程，并阐明了在感知拥挤和顶叶忽视期间有意识的物体视觉和识别可能如何失败。学习类别普遍性取决于由基底核通过乙酰胆碱调节的警觉过程。警觉可能会卡在过高或过低的值上，从而在自闭症和内侧颞叶失忆症中导致学习问题。类似的突触学习规律支持质上不同的行为：颞下皮质中的不变物体类别学习；空间导航期间内嗅皮质和海马皮质中网格细胞和位置细胞的学习；以及在适应性定时条件反射（包括痕迹条件反射）期间内嗅-海马系统中时间细胞的学习。通过内侧和外侧内嗅-海马系统的空间和时间过程似乎是通过同源电路设计进行的。共享的层状新皮质电路设计的变体已被用于模拟三维视觉、语音感知以及认知工作记忆和学习。一种互补的抑制性匹配和失配学习控制运动。本文是名为“大脑与记忆”的特刊的一部分。
