Affiliation:
1. International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, The University of Tokyo , Tokyo 113-0033 , Japan
2. Department of Neurosurgery , The University of Tokyo , Tokyo 113-0033 , Japan
Abstract
Abstract
There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose “Mental Schema 2.0,” a new computational property underlying the brain’s unique learning ability that can be implemented in ANNs.
Funder
World Premier International Research Center Initiative (WPI), MEXT, Japan
Cited by
1 articles.
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1. A Theory of Mental Frameworks;Frontiers in Psychology;2023-07-20