Can human brain connectivity explain verbal working memory?

IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Network-Computation in Neural Systems Pub Date : 2024-11-12 DOI:10.1080/0954898X.2024.2421196
Maxime Carriere, Rosario Tomasello, Friedemann Pulvermüller
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Abstract

The ability of humans to store spoken words in verbal working memory and build extensive vocabularies is believed to stem from evolutionary changes in cortical connectivity across primate species. However, the underlying neurobiological mechanisms remain unclear. Why can humans acquire vast vocabularies, while non-human primates cannot? This study addresses this question using brain-constrained neural networks that realize between-species differences in cortical connectivity. It investigates how these structural differences support the formation of neural representations for spoken words and the emergence of verbal working memory, crucial for human vocabulary building. We develop comparative models of frontotemporal and occipital cortices, reflecting human and non-human primate neuroanatomy. Using meanfield and spiking neural networks, we simulate auditory word recognition and examine verbal working memory function. The "human models", characterized by denser inter-area connectivity in core language areas, produced larger cell assemblies than the "monkey models", with specific topographies reflecting semantic properties of the represented words. Crucially, longer-lasting reverberant neural activity was observed in human versus monkey architectures, compatible with robust verbal working memory, a necessary condition for vocabulary building. Our findings offer insights into the structural basis of human-specific symbol learning and verbal working memory, shedding light on humans' unique capacity for large vocabulary acquisition.

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人脑连通性能否解释言语工作记忆?
人类能够在言语工作记忆中存储口语词汇并建立广泛的词汇量,这被认为源于灵长类动物大脑皮层连接性的进化变化。然而,其潜在的神经生物学机制仍不清楚。为什么人类可以获得大量词汇,而非人灵长类动物却不能?这项研究利用大脑约束神经网络来解决这个问题,该网络实现了大脑皮层连通性的物种间差异。它研究了这些结构性差异如何支持口语词汇神经表征的形成以及对人类词汇积累至关重要的言语工作记忆的出现。我们建立了额颞叶和枕叶皮层的比较模型,反映了人类和非人灵长类的神经解剖学。利用均值场和尖峰神经网络,我们模拟了听觉单词识别并研究了言语工作记忆功能。与 "猴子模型 "相比,"人类模型 "以核心语言区更密集的区间连接为特征,产生了更大的细胞集合,其特定拓扑反映了所代表单词的语义属性。最重要的是,在人类与猴子的结构中观察到了持续时间更长的混响神经活动,这与强大的语言工作记忆相一致,而语言工作记忆是词汇构建的必要条件。我们的研究结果为人类特有的符号学习和言语工作记忆的结构基础提供了见解,揭示了人类获取大量词汇的独特能力。
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来源期刊
Network-Computation in Neural Systems
Network-Computation in Neural Systems 工程技术-工程:电子与电气
CiteScore
3.70
自引率
1.30%
发文量
22
审稿时长
>12 weeks
期刊介绍: Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.
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