Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes may play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales 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. We pose a general model of neuron-synapse-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 homogeneous networks and conventional non-network-based bandit algorithms. Our results indicate how the presence of neuron-astrocyte interaction in the brain may benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics.

翻译：星形胶质细胞是一种普遍存在且神秘的非神经元细胞，存在于所有脊椎动物的大脑中。传统观点认为它们仅对神经元起支持作用，但越来越多的研究认识到星形胶质细胞可能在脑功能和神经计算中发挥更直接、更活跃的作用。由于星形胶质细胞对多种生理协变量敏感，并能以较慢的时间尺度调节神经元活动和连接性，它们可能特别适合以功能显著的方式调节神经回路动力学。在本文中，我们试图通过神经元-星形胶质细胞相互作用的计算模型中的可操作抽象来捕捉这些特征。具体而言，我们探讨了在不同时间尺度上作用的神经元-星形胶质细胞相互作用的嵌套反馈循环，如何赋予星形胶质细胞在上下文依赖环境中实现学习的能力，其中任务参数的波动可能比任务内部需求慢得多。我们提出了一个神经元-突触-星形胶质细胞相互作用的通用模型，并通过形式分析来表征星形胶质细胞的调节如何构成一种元可塑性形式，从而改变突触和神经元随时间适应的方式。随后，我们将该模型嵌入基于bandit的强化学习任务环境中，并展示了时间尺度分离的星形胶质细胞调节如何使网络能够在多个波动上下文中进行学习。事实上，与动态同质网络和传统的非网络bandit算法相比，这些网络的学习可靠性显著提高。我们的结果表明，大脑中神经元-星形胶质细胞相互作用的存在可能有利于不同时间尺度上的学习，并将任务相关的上下文信息传递到回路动力学中。