Universidad de Buenos Aires, Facultad de Ingeniería, Instituto de Ingeniería Biomédica, Buenos Aires, Argentina.
Argonne National Laboratory, Lemont, IL, United States.
Front Neural Circuits. 2020 Apr 16;14:12. doi: 10.3389/fncir.2020.00012. eCollection 2020.
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.
心理语言学的普遍共识认为，在在线句子处理过程中，语法和意义是精确而快速地统一的。尽管有几种理论提出了关于这种现象的神经计算基础的论点，但我们认为，这些理论可以通过包括关于皮质动力学约束的神经生理学数据来受益。此外，一些理论提倡在神经组织中整合复杂的优化方法。在本文中，我们尝试通过引入一个受皮质组织动力学启发的计算模型来填补这些空白。在我们的建模方法中，近端传入树突产生随机的细胞激活，而另一方面，远端树突分支通过树突棘独立地为体细胞去极化做出贡献，最后，预测失败会产生大量的放电事件，从而阻止稀疏分布式表示的形成。本文提出的模型结合了语义和粗粒度的句法约束，用于句子上下文的每个单词，直到语法上相关的单词功能通过来自不同来源的词汇信息的单纯相关性自动出现区分，而无需应用复杂的优化方法。通过支持向量机技术，我们表明，我们的方法返回的稀疏激活特征非常适合-从词嵌入机制返回的特征开始-完成句子中单个单词的语法功能分类。通过这种方式，我们为皮质中与语言相关的统一过程提供了一种受生物启发的计算解释，将心理语言学与语言的神经生物学解释联系起来。我们还声称，本研究中建立的计算假设可以促进未来用于自然语言处理应用的受生物启发的学习算法的工作。
