What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated.
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.
Google DeepMind, 5 New Street Square, London EC4A 3TW, UK; Institute of Cognitive Neuroscience, University College London, 17 Queen Square, WC1N 3AR, UK. Electronic address: dkumaran@google.com.
Dharshan
D
Kumaran
Google DeepMind, 5 New Street Square, London EC4A 3TW, UK; Gatsby Computational Neuroscience Unit, 17 Queen Square, London WC1N 3AR, UK. Electronic address: demishassabis@google.com.
Demis
D
Hassabis
Department of Psychology and Center for Mind, Brain, and Computation, Stanford University, 450 Serra Mall, CA 94305, USA. Electronic address: mcclelland@stanford.edu.
James L
JL
McClelland
D000818:Animals; D006624:Hippocampus / Q000502:physiology; D006801:Humans; D007360:Intelligence; D007858:Learning; D008568:Memory / Q000502:physiology; D008959:Models, Neurological; D019579:Neocortex / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology; D013598:Systems Theory
artificial intelligence; hippocampus; learning; memory