Sentences as connection paths: A neural language architecture of sentence structure in the brain

Abstract: This article presents a neural language architecture of sentence structure in the brain, in which sentences are temporal connection paths that interconnect neural structures underlying their words. Words remain 'in-situ', hence they are always content-addressable. Arbitrary and novel sentences (with novel words) can be created with 'neural blackboards' for words and sentences. Hence, the unlimited productivity of natural language can be achieved with a 'fixed' small world like network structure. The article focuses on the neural blackboard for sentences. The architecture uses only one 'connection matrix' for binding all structural relations between words in sentences. Its ability to represent arbitrary (English) sentences is discussed in detail, based on a comprehensive analysis of them. The architecture simulates intra-cranial brain activity observed during sentence processing and fMRI observations related to sentence complexity and ambiguity. The simulations indicate that the observed effects relate to global control over the architecture, not to the sentence structures involved, which predicts higher activity differences related to complexity and ambiguity with higher comprehension capacity. Other aspects discussed are the 'intrinsic' sentence structures provided by connection paths and their relation to scope and inflection, the use of a dependency parser for control of binding, long-distance dependencies and gaps, question answering, ambiguity resolution based on backward processing without explicit backtracking, garden paths, and performance difficulties related to embeddings.