Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks

@article{Soltoggio2018BornTL,
  title={Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks},
  author={Andrea Soltoggio and Kenneth O. Stanley and Sebastian Risi},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2018},
  volume={108},
  pages={
          48-67
        }
}
Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifelong learning. The interplay of these elements leads to the emergence of biological intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) employ simulated evolution in-silico to breed plastic neural networks with the aim to autonomously design and create learning systems. EPANN experiments evolve networks that… Expand
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