What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated

被引:405
作者
Kumaran, Dharshan [1 ,2 ]
Hassabis, Demis [1 ,3 ]
McClelland, James L. [4 ,5 ]
机构
[1] Google DeepMind, 5 New St Sq, London EC4A 3TW, England
[2] UCL, Inst Cognit Neurosci, 17 Queen Sq, London WC1N 3AR, England
[3] Gatsby Computat Neurosci Unit, 17 Queen Sq, London WC1N 3AR, England
[4] Stanford Univ, Dept Psychol, 450 Serra Mall, Stanford, CA 94305 USA
[5] Stanford Univ, Ctr Mind Brain & Computat, 450 Serra Mall, Stanford, CA 94305 USA
关键词
MEDIAL TEMPORAL-LOBE; PATTERN SEPARATION; HIPPOCAMPAL REPLAY; MEMORY CONSOLIDATION; SPATIAL MEMORY; DENTATE GYRUS; PLACE CELLS; COMPUTATIONAL PRINCIPLES; INTERACTIVE ACTIVATION; CONNECTIONIST MODELS;
D O I
10.1016/j.tics.2016.05.004
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
We update complementary learning systems (CLS) theory, which holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences. We broaden the role of replay of hippocampal memories in the theory, noting that replay allows goal-dependent weighting of experience statistics. We also address recent challenges to the theory and extend it by showing that recurrent activation of hippocampal traces can support some forms of generalization and that neocortical learning can be rapid for information that is consistent with known structure. Finally, we note the relevance of the theory to the design of artificial intelligent agents, highlighting connections between neuroscience and machine learning.
引用
收藏
页码:512 / 534
页数:23
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