论文标题
增量学习的丘脑皮层尖峰模型，结合感知，上下文和NREM-sleep介导的噪声耐力
Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep-mediated noise-resilience
论文作者
论文摘要
大脑从几个嘈杂的示例中表现出快速增量学习的能力，以及在自主创建类别中相似的记忆并将上下文提示与感觉知觉相结合的能力。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，关于不同大脑状态的基本过程和特定作用知之甚少。在这项工作中，我们利用了基于兴奋性和抑制性尖峰神经元的全部胜利者回路的丘脑皮层模型中上下文和感知的组合。在校准了该模型以表达具有与生物学措施相当的特征表达清醒和深度睡眠状态之后，我们证明了从几个示例中快速递增学习的模型能力，其弹性和上下文信号提出的弹性，以及由于诱发的触发稳态和类似记忆的诱导的诱导的诱导的诱导的视觉分类而改善了视觉分类。
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.
