A fundamental aspect of learning in biological neural networks (BNNs) 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 based on the local activation of neurons. In this work, we employ genetic algorithms to evolve local learning rules, from Hebbian perspective, to produce autonomous learning under changing environmental conditions. Our evolved synaptic plasticity rules are capable of performing synaptic updates in distributed and self-organized fashion, based only on the binary activation states of neurons, and a reinforcement signal received from the environment. We demonstrate the learning and adaptation capabilities of the ANNs modified by the evolved plasticity rules on a foraging task in a continuous learning settings. Our results show that evolved plasticity rules are highly efficient at adapting the ANNs to task under changing environmental conditions.

中文翻译：

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不断变化的环境条件下自主学习的可塑性

生物神经网络 (BNN) 学习的一个基本方面是可塑性，它允许它们在其生命周期内修改其配置。Hebbian 学习是一种生物学上合理的机制，用于基于神经元的局部激活对可塑性进行建模。在这项工作中，我们采用遗传算法从 Hebbian 的角度进化局部学习规则，以在不断变化的环境条件下产生自主学习。我们进化的突触可塑性规则能够以分布式和自组织方式执行突触更新，仅基于神经元的二元激活状态和从环境接收的强化信号。我们展示了 ANN 的学习和适应能力，这些能力由不断学习环境中的觅食任务的进化可塑性规则修改。我们的结果表明，进化的可塑性规则在使人工神经网络适应不断变化的环境条件下的任务方面非常有效。