Center-Embedding and Constituency in the Brain and a New Characterization of Context-Free Languages

TL;DR

This paper demonstrates that neural systems can process complex language structures like constituency and center embedding, leading to a new understanding of context-free languages within neural computation.

Contribution

It introduces neural implementations for constituency and center embedding, offering a novel characterization of context-free languages compatible with brain function.

Findings

Neural models can identify key sentence parts such as verb phrases.

Neural systems can process center-embedded sentences.

A new characterization of context-free languages is proposed.

Abstract

A computational system implemented exclusively through the spiking of neurons was recently shown capable of syntax, that is, of carrying out the dependency parsing of simple English sentences. We address two of the most important questions left open by that work: constituency (the identification of key parts of the sentence such as the verb phrase) and the processing of dependent sentences, especially center-embedded ones. We show that these two aspects of language can also be implemented by neurons and synapses in a way that is compatible with what is known, or widely believed, about the structure and function of the language organ. Surprisingly, the way we implement center embedding points to a new characterization of context-free languages.