Investigating the Evolution of a Neuroplasticity Network for Learning
通过模拟觅食环境中的虚拟生物，研究演化能否塑造通用的神经可塑性机制，使其成为有利的学习规则，并发现演化出的可塑性网络优于参数化赫布机制。
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 brainand movement-related energy penalty terms in the fitness function.