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Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

机译：改变环境条件下的自主学习的不塑性

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摘要

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.
机译：生物网络中学习的基本方面是塑性性质，其允许它们在寿命期间修改它们的配置。休斯学习是一种生物学典雅的机制，用于基于神经元的局部相互作用来建立人工神经网络（ANNS）中的可塑性。然而，来自当地Hebbian可塑性规则的连贯的全球学习行为的出现并不是很好地理解。这项工作的目标是发现可以提供自主全球学习的可解释的本地Hebbian学习规则。为此，我们使用离散表示来在有限搜索空间中对学习规则进行编码。然后基于神经元的局部相互作用来使用这些规则来执行突触变化。我们使用遗传算法来优化这些规则，以允许在在线终身学习设置中允许在两个单独的任务（觅食和捕食者方案）上学习。由此产生的进化规则会聚成一组明确的定义可解释类型，这是完全讨论的。值得注意的是，这些规则的表现，同时在学习任务期间调整ANN，与山坡等离线学习方法的表现相当。

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