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

Contrastive self-supervised learning has gained attention for its ability to
create high-quality representations from large unlabelled data sets. A key
reason that these powerful features enable data-efficient learning of
downstream tasks is that they provide augmentation invariance, which is often a
useful inductive bias. However, the amount and type of invariances preferred is
not known apriori, and varies across different downstream tasks. We therefore
propose a multi-task self-supervised framework (MT-SLVR) that learns both
variant and invariant features in a parameter-efficient manner. Our multi-task
representation provides a strong and flexible feature that benefits diverse
downstream tasks. We evaluate our approach on few-shot classification tasks
drawn from a variety of audio domains and demonstrate improved classification
performance on all of them

提出了一个名为 MT-SLVR 的多任务自我监督框架，用于学习变化和不变性特征，提供了强大和灵活的功能，改善了多种音频领域的少样本分类任务分类性能。