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首页> 外文期刊>Journal of Computational Neuroscience >Synaptic efficacy shapes resource limitations in working memory
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Synaptic efficacy shapes resource limitations in working memory

机译：突触功效影响工作记忆中的资源限制

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摘要

Working memory (WM) is limited in its temporal length and capacity. Classic conceptions of WM capacity assume the system possesses a finite number of slots, but recent evidence suggests WM may be a continuous resource. Resource models typically assume there is no hard upper bound on the number of items that can be stored, but WM fidelity decreases with the number of items. We analyze a neural field model of multi-item WM that associates each item with the location of a bump in a finite spatial domain, considering items that span a one-dimensional continuous feature space. Our analysis relates the neural architecture of the network to accumulated errors and capacity limitations arising during the delay period of a multi-item WM task. Networks with stronger synapses support wider bumps that interact more, whereas networks with weaker synapses support narrower bumps that are more susceptible to noise perturbations. There is an optimal synaptic strength that both limits bump interaction events and the effects of noise perturbations. This optimum shifts to weaker synapses as the number of items stored in the network is increased. Our model not only provides a circuit-based explanation for WM capacity, but also speaks to how capacity relates to the arrangement of stored items in a feature space.
机译：工作内存（WM）的时间长度和容量有限。 WM容量的经典概念假定系统拥有有限数量的插槽，但是最近的证据表明WM可能是连续的资源。资源模型通常假定可以存储的项目数量没有硬性上限，但是WM保真度随项目数量而降低。我们分析了多项目WM的神经场模型，该模型将每个项目与有限空间域中的凹凸位置相关联，并考虑了跨越一维连续特征空间的项目。我们的分析将网络的神经体系结构与在多项目WM任务的延迟期间产生的累积错误和容量限制联系在一起。突触更强的网络支持更宽的碰撞，而相互作用更多，而突触更弱的网络则支持更窄的碰撞，更容易受到噪声的干扰。有一个最佳的突触强度，它既可以限制碰撞相互作用事件，也可以限制噪声干扰的影响。随着网络中存储的项目数量的增加，此最佳状态将转换为较弱的突触。我们的模型不仅为WM容量提供了基于电路的解释，而且还谈到了容量与要素空间中存储项目的排列之间的关系。

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