A whole-task brain model of associative recognition that accounts for human behavior and neuroimaging data.

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2023-09-08 eCollection Date: 2023-09-01 DOI:10.1371/journal.pcbi.1011427

Jelmer P Borst, Sean Aubin, Terrence C Stewart

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Abstract

Brain models typically focus either on low-level biological detail or on qualitative behavioral effects. In contrast, we present a biologically-plausible spiking-neuron model of associative learning and recognition that accounts for both human behavior and low-level brain activity across the whole task. Based on cognitive theories and insights from machine-learning analyses of M/EEG data, the model proceeds through five processing stages: stimulus encoding, familiarity judgement, associative retrieval, decision making, and motor response. The results matched human response times and source-localized MEG data in occipital, temporal, prefrontal, and precentral brain regions; as well as a classic fMRI effect in prefrontal cortex. This required two main conceptual advances: a basal-ganglia-thalamus action-selection system that relies on brief thalamic pulses to change the functional connectivity of the cortex, and a new unsupervised learning rule that causes very strong pattern separation in the hippocampus. The resulting model shows how low-level brain activity can result in goal-directed cognitive behavior in humans.

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联想识别的全任务大脑模型，用于解释人类行为和神经成像数据。

大脑模型通常关注低水平的生物细节或定性的行为影响。相反，我们提出了一个生物上合理的联想学习和识别尖峰神经元模型，该模型考虑了整个任务中人类行为和低水平大脑活动。基于认知理论和对M/EEG数据的机器学习分析，该模型经历了五个处理阶段：刺激编码、熟悉度判断、联想检索、决策和运动反应。结果与人类的反应时间和枕叶、颞叶、前额叶和中央前脑区域的源定位脑磁图数据相匹配；以及前额叶皮层的经典fMRI效应。这需要两个主要的概念进步：一个是基底节-丘脑动作选择系统，它依赖短暂的丘脑脉冲来改变皮层的功能连接，另一个是新的无监督学习规则，它会在海马体中引起非常强的模式分离。由此产生的模型显示了低水平的大脑活动如何导致人类目标导向的认知行为。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology 生物-生化研究方法

CiteScore

7.10

自引率

4.70%

发文量

820

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