Brennan Connor, Proekt Alex
University of Pennsylvania, 3160 Chestnut St., Philadelphia, PA, USA.
Commun Psychol. 2023;1. doi: 10.1038/s44271-023-00027-8. Epub 2023 Oct 25.
Most cognitive functions require the brain to maintain immediately preceding stimuli in working memory. Here, using a human working memory task with multiple delays, we test the hypothesis that working memories are stored in a discrete set of stable neuronal activity configurations called attractors. We show that while discrete attractor dynamics can approximate working memory on a single time scale, they fail to generalize across multiple timescales. This failure occurs because at longer delay intervals the responses contain more information about the stimuli than can be stored in a discrete attractor model. We present a modeling approach that combines discrete attractor dynamics with activity-dependent plasticity. This model successfully generalizes across all timescales and correctly predicts intertrial interactions. Thus, our findings suggest that discrete attractor dynamics are insufficient to model working memory and that activity-dependent plasticity improves durability of information storage in attractor systems.
大多数认知功能都要求大脑在工作记忆中保留紧接在前的刺激。在此，我们使用一个具有多个延迟的人类工作记忆任务，来检验工作记忆存储于一组离散的稳定神经元活动构型（即吸引子）这一假设。我们发现，虽然离散吸引子动力学在单个时间尺度上可以近似工作记忆，但它们无法在多个时间尺度上进行推广。这种失败的发生是因为在较长的延迟间隔下，反应中包含的关于刺激的信息比离散吸引子模型所能存储的更多。我们提出了一种将离散吸引子动力学与活动依赖可塑性相结合的建模方法。该模型成功地在所有时间尺度上进行了推广，并正确预测了试验间的相互作用。因此，我们的研究结果表明，离散吸引子动力学不足以对工作记忆进行建模，而活动依赖可塑性提高了吸引子系统中信息存储的耐久性。
