Department of Electrical and Systems Engineering, Washington University, St. Louis, Missouri, United States of America.
Department of Mechanical Engineering, University of California, Riverside, California, United States of America.
PLoS Comput Biol. 2024 May 31;20(5):e1012186. doi: 10.1371/journal.pcbi.1012186. eCollection 2024 May.
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 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 compared to dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results fuel the notion that neuron-astrocyte interactions in the brain benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics.
星形胶质细胞是一种普遍存在且神秘的非神经元细胞类型，存在于所有脊椎动物的大脑中。虽然传统上认为星形胶质细胞对神经元起支持作用，但越来越多的证据表明，星形胶质细胞在大脑功能和神经计算中发挥着更直接和积极的作用。由于它们对许多生理变量的敏感性，以及在较慢的时间尺度上调节神经元活动和连接的能力，星形胶质细胞可能特别适合以功能显著的方式调节神经回路的动态。在当前的论文中，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。具体来说，我们研究了神经元-星形胶质细胞相互作用的嵌套反馈回路如何在不同的时间尺度上发挥作用，从而赋予星形胶质细胞在上下文相关的环境中学习的能力，在这种环境中，任务参数的波动可能比任务要求的波动慢得多。我们提出了一个神经元-突触-星形胶质细胞相互作用的一般模型，并使用形式分析来描述星形胶质细胞的调节如何构成一种形式的元可塑性，改变了突触和神经元随时间适应的方式。然后，我们将这个模型嵌入一个基于带臂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的环境中进行学习。事实上，与动态同质网络和传统的非网络强化学习算法相比，这些网络的学习可靠性要高得多。我们的结果支持了这样一种观点，即大脑中的神经元-星形胶质细胞相互作用有利于在不同的时间尺度上进行学习，并将与任务相关的上下文信息传递到电路动力学中。
