Yaman Anil, Iacca Giovanni, Mocanu Decebal Constantin, Coler Matt, Fletcher George, Pechenizkiy Mykola
Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, 5612 AP, the NetherlandsDepartment of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
Department of Information Engineering and Computer Science, University of Trento, Trento, 38122, Italy
Evol Comput. 2021 Sep 1;29(3):391-414. doi: 10.1162/evco_a_00286.
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.
生物神经网络学习的一个基本方面是可塑性特性，这使它们能够在其生命周期内修改自身配置。赫布学习是一种基于神经元局部相互作用，在人工神经网络（ANN）中对可塑性特性进行建模的生物学上合理的机制。然而，从局部赫布可塑性规则中出现连贯的全局学习行为这一点还没有得到很好的理解。这项工作的目标是发现可解释的局部赫布学习规则，这些规则能够提供自主的全局学习。为了实现这一目标，我们使用离散表示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，这些规则被用于执行突触变化。我们采用遗传算法来优化这些规则，以便在在线生命周期学习设置中对两个不同的任务（觅食和捕食者 - 猎物场景）进行学习。最终得到的进化规则收敛为一组定义明确且可解释的类型，并对其进行了深入讨论。值得注意的是，这些规则在学习任务中使人工神经网络适应时的性能，与诸如爬山法等离线学习方法的性能相当。
