Bernoulli Institute, University of Groningen; Groningen, The Netherlands.
Centre for Theoretical Neuroscience, University of Waterloo; Waterloo, Ontario, Canada.
PLoS Comput Biol. 2023 Sep 8;19(9):e1011427. doi: 10.1371/journal.pcbi.1011427. eCollection 2023 Sep.
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.
大脑模型通常要么专注于低水平的生物细节，要么专注于定性的行为效应。相比之下，我们提出了一个基于生物合理性的、用于联想学习和识别的尖峰神经元模型，它既能解释人类行为，又能解释整个任务中的低水平大脑活动。该模型基于认知理论和从 M/EEG 数据分析中得到的机器学习见解，通过五个处理阶段进行：刺激编码、熟悉度判断、联想检索、决策和运动反应。该模型的结果与人类的反应时间以及枕叶、颞叶、前额叶和中央前回的源定位 MEG 数据相匹配；同时还与前额叶皮层中的经典 fMRI 效应相匹配。这需要两个主要的概念性进展：一个基底神经节-丘脑的动作选择系统，它依赖于短暂的丘脑脉冲来改变皮层的功能连接；以及一个新的无监督学习规则，它导致海马体中非常强烈的模式分离。由此产生的模型展示了低水平的大脑活动如何导致人类的目标导向认知行为。
