Astrocytes are a highly expressed and highly enigmatic cell-type in the mammalian brain. Traditionally viewed as a mediator of basic physiological sustenance, it is increasingly recognized that astrocytes may play a more direct role in neural computation. A conceptual challenge to this idea is the fact that astrocytic activity takes a very different form than that of neurons, and in particular, occurs at orders-of-magnitude slower time-scales. In the current paper, we engage how such time-scale separation may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. This idea is based on the recent supposition that astrocytes, owing to their sensitivity to a host of physiological covariates, may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. We pose a general model of neural-synaptic-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably versus dynamically homogenous networks and conventional non-network-based bandit algorithms. Our results indicate how the presence of neural-astrocyte interaction in the brain may benefit learning over different time-scale and the conveyance of task relevant contextual information onto circuit dynamics.

星形胶质细胞在哺乳动物大脑中是一种高度表达和高度神秘的细胞类型。本文探讨了星形胶质细胞的时间尺度分离如何赋予其在上下文相关环境中引发学习的能力，以及这种新陈代谢调节如何对神经电路动力学产生影响，进而实现元可塑性。同时，通过在基于赌博机的强化学习任务环境中嵌入这一模型，展示了时间尺度分离的星形胶质细胞调节能力如何使得网络在多个波动的环境中学习更加可靠，相对于动态同质网络和传统非网络基础的赌博机算法。研究结果揭示了星形胶质细胞与神经元之间相互作用如何促进大脑在不同时间尺度上的学习，并将任务相关的上下文信息传递给神经电路动力学。

星形胶质细胞作为元可塑性和上下文引导的网络功能机制