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- W1977301325 abstract "The present paper analyzes the mutual relationships between generative and developmental systems (GDS) and synaptic plasticity when evolving plastic artificial neural networks (ANNs) in reward-based scenarios. We first introduce the concept of synaptic Transitive Learning Abilities (sTLA), which reflects how well an evolved plastic ANN can cope with learning scenarios not encountered during the evolution process. We subsequently report results of a set of experiments designed to check that (1) synaptic plasticity can help a GDS to fine-tune synaptic weights and (2) that with the investigated generative encoding (EvoNeuro), only a few learning scenarios are necessary to evolve a general learning system, which can adapt itself to reward-based scenarios not tested during the fitness evaluation." @default.
- W1977301325 created "2016-06-24" @default.
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- W1977301325 date "2011-07-12" @default.
- W1977301325 modified "2023-10-18" @default.
- W1977301325 title "On the relationships between synaptic plasticity and generative systems" @default.
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- W1977301325 doi "https://doi.org/10.1145/2001576.2001782" @default.
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