Dipartimento di Fisica, Università di Cagliari, Cagliari, Italy.
Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Cagliari, Italy.
PLoS Comput Biol. 2021 Jun 28;17(6):e1009045. doi: 10.1371/journal.pcbi.1009045. eCollection 2021 Jun.
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.
大脑表现出从少量嘈杂示例中快速增量学习的能力，以及将相似记忆自动关联到自主创建的类别中并将上下文提示与感官感知相结合的能力。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，对于潜在的过程和不同大脑状态的具体作用知之甚少。在这项工作中，我们利用了丘脑-皮层模型中的上下文和感知的组合，该模型基于兴奋性和抑制性尖峰神经元的软胜者全取电路。在将该模型校准为以与生物测量相当的特征表达清醒和深度睡眠状态后，我们证明了该模型从少量示例中快速增量学习的能力、在接收到嘈杂的感知和上下文信号时的弹性，以及在睡眠后由于诱导的突触平衡和相似记忆的关联而导致视觉分类得到改善。
