Investigating the Evolution of a Neuroplasticity Network for Learning

The processes of evolution and learning interact. Learning is an evolved strategy that improves fitness, especially in a world where some aspects cannot realistically be encoded in the genome. We endeavored to see if evolution could sculpt a generic neuroplasticity mechanism into a learning rule that would give virtual organisms an advantage in a simulated foraging environment. Our virtual organisms have brains with nine neurons. The connections between those neurons are adjusted by a plasticity rule that is computed by another fixed neural network. Evolution experiments repeatedly found plasticity networks that conferred an adaptive advantage, even outperforming populations that were given a parametric Hebbian plasticity mechanism. Evolution also favored the inclusion of genetically encoded heterogeneity. We also investigate how behavior is influenced by various brain- and movement-related energy penalty terms in the fitness function.

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