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.

提出了一个受海马体和前额叶皮质启发的生物学上可行的元学习模型，其使用尖峰神经网络和基于奖励的学习系统来实现在低数据条件下的快速学习并避免了灾难性遗忘，并且可以轻松地应用于脉冲神经形态学设备和在 few-shot 分类任务中展示了其与现有技术的竞争力。

奖励调节 STDP 的突触可塑性神经元脉冲伪学习

Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP

Few-shot classification aims to recognize unseen classes with few labeled
samples from each class. Many meta-learning models for few-shot classification
elaborately design various task-shared inductive bias (meta-knowledge) to solve
such tasks, and achieve impressive performance. However, when there exists the
domain shift between the training tasks and the test tasks, the obtained
inductive bias fails to generalize across domains, which degrades the
performance of the meta-learning models. In this work, we aim to improve the
robustness of the inductive bias through task augmentation. Concretely, we
consider the worst-case problem around the source task distribution, and
propose the adversarial task augmentation method which can generate the
inductive bias-adaptive 'challenging' tasks. Our method can be used as a simple
plug-and-play module for various meta-learning models, and improve their
cross-domain generalization capability. We conduct extensive experiments under
the cross-domain setting, using nine few-shot classification datasets:
mini-ImageNet, CUB, Cars, Places, Plantae, CropDiseases, EuroSAT, ISIC and
ChestX. Experimental results show that our method can effectively improve the
few-shot classification performance of the meta-learning models under domain
shift, and outperforms the existing works. Our code is available at
this https URL

针对 few-shot 分类在训练和测试分布之间的域变化导致在测试上性能下降的问题，提出了通过任务增强来改善归纳偏置的鲁棒性，具体来说，采用对抗任务增强方法来生成具有挑战性的任务，可以提供简单的即插即用模块来提高元学习模型在跨域通用性中的性能。