Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

@article{Yaman2021EvolvingPF,
  title={Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions},
  author={Anil Yaman and Decebal Constantin Mocanu and Giovanni Iacca and Matt Coler and G. Fletcher and Mykola Pechenizkiy},
  journal={Evolutionary Computation},
  year={2021},
  volume={29},
  pages={391-414}
}
Abstract A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to… 
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