Synergistic short-term synaptic plasticity mechanisms for working memory

Abstract

Working memory (WM) is essential for almost every cognitive task. The neural and synaptic mechanisms supporting the rapid encoding and maintenance of memories in diverse tasks are the subject of an ongoing debate. The traditional view of WM as stationary persistent firing of selective neuronal populations has given room to newer ideas regarding mechanisms that support a more dynamic maintenance of multiple items. Various computational WM models based on different biologically plausible plasticity mechanisms have been proposed. We show that these proposed short-term plasticity mechanisms may not necessarily be competing explanations, but instead yield interesting interactions that broaden the functional range of models on a wide set of WM task motifs and simultaneously enhance the biological plausibility of spiking neural network models, in particular of the underlying synaptic plasticity. While reductionist models (WM function explained by one particular mechanism) are theoretically appealing and have increased our understanding of specific mechanisms, they are narrow explanations. In this study we evaluate the interactions between three commonly proposed classes of plasticity, namely intrinsic excitability, synaptic facilitation/augmentation and Hebbian plasticity. We systematically test combinations of mechanisms in a spiking neural network model on a broad suite of tasks or functional motifs deemed principally important for WM operation, such as one-shot encoding, free and cued recall, multi-item delay maintenance and updating. Our analysis of the operational task performance indicates that a composite model is superior to more reductionist variants. Importantly, we attribute the observable differences to the principle nature of specific types of plasticity. Significance StatementWorking memory - the ability to temporarily hold and manipulate information - is fundamental to cognition, yet the underlying neural and synaptic mechanisms remain debated. Traditional models propose and investigate isolated mechanisms like synaptic facilitation, often applied to specific tasks or rather simple delay maintenance. In contrast, this study proposes that broader working memory capabilities emerge from synergistic interactions among three commonly proposed short-term plasticity mechanisms: Hebbian learning, synaptic facilitation/augmentation and intrinsic excitability. Using biologically constrained spiking neural networks across a broad synthetic task battery, we show that combining mechanisms produces superior working memory capabilities compared with single isolated mechanism. This computational framework advances our understanding of how multiple plasticity mechanisms coordinate to support flexible, and robust working memory.