The human brain constantly learns and rapidly adapts to new situations by integrating acquired knowledge and experiences into memory. Developing this capability in machine learning models is considered an important goal of AI research since deep neural networks perform poorly when there is limited data or when they need to adapt quickly to new unseen tasks. Meta-learning models are proposed to facilitate quick learning in low-data regimes by employing absorbed information from the past. Although some models have recently been introduced that reached high-performance levels, they are not biologically plausible. We have proposed a bio-plausible meta-learning model inspired by the hippocampus and the prefrontal cortex using spiking neural networks with a reward-based learning system. Our proposed model includes a memory designed to prevent catastrophic forgetting, a phenomenon that occurs when meta-learning models forget what they have learned as soon as the new task begins. Also, our new model can easily be applied to spike-based neuromorphic devices and enables fast learning in neuromorphic hardware. The final analysis will discuss the implications and predictions of the model for solving few-shot classification tasks. In solving these tasks, our model has demonstrated the ability to compete with the existing state-of-the-art meta-learning techniques.

翻译：人类大脑通过将获得的知识和经验整合到记忆中，不断学习并快速适应新情境。在机器学习模型中开发这种能力被视为人工智能研究的重要目标，因为深度神经网络在数据有限或需要快速适应未见新任务时表现不佳。元学习模型通过利用从过去吸收的信息，旨在促进低数据情形下的快速学习。尽管近期已有部分模型达到高性能水平，但它们缺乏生物 plausibility。我们受海马体和前额叶皮层启发，提出了一种基于脉冲神经网络和奖赏学习系统的生物 plausible 元学习模型。该模型包含一个旨在防止灾难性遗忘的记忆模块——灾难性遗忘是指元学习模型在新任务开始时立即忘记已学知识的现象。此外，我们的新模型可轻松应用于基于脉冲的神经形态器件，并在神经形态硬件上实现快速学习。最终分析将讨论该模型在解决少样本分类任务中的意义与预测。在解决这些任务时，我们的模型展现出与现有最先进元学习技术相竞争的能力。