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- W2144757311 abstract "In this paper we study age-varying plasticities across different components in an artificial neural network performing a reinforcement learning task. An evolutionary algorithm is given the task of mapping the age of agents to the plasticity levels of different network components. The results show that patterns of plasticity resembling biological sensitive periods appear, and that these periods schedule learning across the components of the network, which leads to a reduction in the total learning effort while retaining the quality of learning. The sequencing of sensitive periods forms a cascade of partiallyoverlapping learning periods, which has been proposed as a way of organizing sensory development of abilities that depend on several interrelated brain functions." @default.
- W2144757311 created "2016-06-24" @default.
- W2144757311 creator A5071893221 @default.
- W2144757311 date "2013-09-02" @default.
- W2144757311 modified "2023-09-27" @default.
- W2144757311 title "Evolved Sensitive Periods in Learning" @default.
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- W2144757311 doi "https://doi.org/10.7551/978-0-262-31709-2-ch059" @default.
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