The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories.

中文翻译：

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结合感知、上下文和 NREM 睡眠介导的噪声弹性的增量学习的丘脑皮质尖峰模型

大脑表现出从少数嘈杂示例中快速增量学习的能力，以及将类似记忆与自主创建的类别相关联并将上下文提示与感官知觉相结合的能力。与睡眠一起，这些机制被认为是许多高级认知功能的关键组成部分。然而，人们对不同大脑状态的潜在过程和具体作用知之甚少。在这项工作中，我们在基于兴奋性和抑制性尖峰神经元的软赢家通吃电路的丘脑皮质模型中利用了上下文和感知的组合。在校准该模型以表达具有与生物测量相当的特征的清醒和深度睡眠状态后，我们从几个例子中证明了快速增量学习的模型能力，