Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “EPANNs are strongly inspired by the biological evidence of natural computation and intelligence (Floreano and Mattiussi, 2008; Downing, 2015).”
Using a map-based encoding to evolve plastic neural networks (2011, background): “Evolved ANNs show impressive results as controllers for autonomous robots and artificial agents [1]–[3].”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “…bottom-up emergence of intelligent behaviors in fields such as artificial life (Langton, 1990) and evolutionary robotics (Floreano and Nolfi, 2004; Floreano and Mattiussi, 2008), the results presented here show that very small structures can serve as exemplary paradigms of adaptive behavior…”
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review (2017, methodology): “Biology has inspired several artificial intelligence techniques such as ANNs, evolutionary algorithms, and cellular automata (Floreano & Mattiussi, 2008).”