A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

心理语言学的一个普遍协议声称，在在线句子处理过程中，语法和含义可以精确而快速地统一在一起。尽管有几种理论对这种现象的神经计算基础有先进的论据，但我们认为，通过将有关脑组织皮质动力学约束的神经生理学数据包括在内，这些理论可能会受益。此外，一些理论促进了神经组织中复杂优化方法的集成。在本文中，我们试图填补这些空白，并引入一个受皮层组织动力学启发的计​​算模型。在我们的建模方法中，近端传入树突产生随机的细胞激活，而远端树突分支（另一方面）通过树突尖峰独立地导致体细胞去极化，最后，预测失败会产生大量的触发事件，从而阻止了稀疏分布表示的形成。本文提出的模型将句子上下文中每个单词的语义约束和粗粒度句法约束结合在一起，直到通过语法语法相关的单词功能辨别自发地通过来自不同来源的词汇信息的唯一关联而出现，而无需应用复杂的优化方法。通过支持向量机技术，我们证明了我们的方法返回的稀疏激活特征非常适合-从Word Embedding机制返回的特征中进行引导-完成句子中单个单词的语法功能分类。通过这种方式，我们为皮层中与语言相关的统一过程开发了生物学指导的计算解释，该过程将心理语言学与语言的神经生物学联系起来。我们还声称，本研究中建立的计算假设可能会促进有关自然语言处理应用的生物学启发式学习算法的未来工作。