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Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing

机译:地层系统中语法结构的实时并行处理:使用储层计算的递归网络模拟研究

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摘要

Sentence processing takes place in real-time. Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. Recent neurophysiological studies in humans suggest that the fronto-striatal system (frontal cortex, and striatum – the major input locus of the basal ganglia) plays a crucial role in this process. The current research provides a possible explanation of how certain aspects of this real-time processing can occur, based on the dynamics of recurrent cortical networks, and plasticity in the cortico-striatal system. We simulate prefrontal area BA47 as a recurrent network that receives on-line input about word categories during sentence processing, with plastic connections between cortex and striatum. We exploit the homology between the cortico-striatal system and reservoir computing, where recurrent frontal cortical networks are the reservoir, and plastic cortico-striatal synapses are the readout. The system is trained on sentence-meaning pairs, where meaning is coded as activation in the striatum corresponding to the roles that different nouns and verbs play in the sentences. The model learns an extended set of grammatical constructions, and demonstrates the ability to generalize to novel constructions. It demonstrates how early in the sentence, a parallel set of predictions are made concerning the meaning, which are then confirmed or updated as the processing of the input sentence proceeds. It demonstrates how on-line responses to words are influenced by previous words in the sentence, and by previous sentences in the discourse, providing new insight into the neurophysiology of the P600 ERP scalp response to grammatical complexity. This demonstrates that a recurrent neural network can decode grammatical structure from sentences in real-time in order to generate a predictive representation of the meaning of the sentences. This can provide insight into the underlying mechanisms of human cortico-striatal function in sentence processing.
机译:句子处理是实时进行的。句子中的前一个单词可能会在数百毫秒的时间范围内影响当前单词的处理。最近在人类进行的神经生理学研究表明,额叶纹状体系统(额叶皮层和纹状体-基底神经节的主要输入基因座)在此过程中起着至关重要的作用。当前的研究基于循环皮层网络的动力学以及皮质-纹状体系统的可塑性,为这种实时处理的某些方面如何发生提供了可能的解释。我们将前额叶区域BA47模拟为循环网络,该网络在句子处理过程中接收有关单词类别的在线输入,其中皮层和纹状体之间具有可塑连接。我们利用皮质-纹状体系统和储层计算之间的同源性,其中复发性额叶皮质网络是储层,而塑料皮质-纹状体突触是读出的。该系统在句子-意思对上进行训练,其中意义被编码为纹状体中的激活,对应于不同名词和动词在句子中扮演的角色。该模型学习了扩展的语法结构集,并展示了将其推广到新颖结构的能力。它说明了在句子的开头有多组关于含义的并行预测,然后随着输入句子的处理进行确认或更新。它演示了对单词的在线响应如何受到句子中先前单词以及话语中先前句子的影响,从而为P600 ERP头皮响应语法复杂性的神经生理学提供了新见解。这证明了递归神经网络可以实时地从句子中解码语法结构,以便生成句子含义的预测表示。这可以提供对句子处理中人类皮质纹状体功能的潜在机制的了解。

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