Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks
This work tackles the problem of designing adaptive AI systems for researchers in machine learning and computational neuroscience, but it is incremental as it primarily reviews existing progress and ideas in the field.
The paper reviews Evolved Plastic Artificial Neural Networks (EPANNs), which use simulated evolution to create adaptive neural systems that can discover novel learning algorithms, addressing the limitations of hand-designed networks by enabling more flexible and innovative solutions.
Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented.