Discrete combinatorial circuits emerging in neural networks: A mechanism for rules of grammar in the human brain?
In neural network research on language, the existence of discrete combinatorial rule representations is commonly denied. Combinatorial capacity of networks and brains is rather attributed to probability mapping and pattern overlay. Here, we demonstrate that networks incorporating relevant features of neuroanatomical connectivity and neuronal function give rise to discrete neuronal circuits that store combinatorial information and exhibit a function similar to elementary rules of grammar. Key properties of these networks are rich auto- and hetero-associative connectivity, availability of sequence detectors similar to those found in a range of animals, and unsupervised Hebbian learning. Input of specific word sequences establishes sequence detectors in the network, and substitutions of words and larger string segments from one syntactic category, occurring in the context of elements of a second syntactic class, lead to binding between them into neuronal assemblies. Critically, these newly formed aggregates of sequence detectors now respond in a discrete generalizing fashion when members of specific substitution classes of string elements are combined with each other. The discrete combinatorial neuronal assemblies (DCNAs) even respond in the same way to learned strings and to word sequences that never appeared in the input but conform to a rule. We also show how combinatorial information interacts with information about functional and anatomical properties of the brain in the emergence of discrete neuronal circuits that may implement rules and discuss the model in the wider context of brain mechanism for syntax and grammar. Implications for the evolution of human language are discussed in closing.
neural network grammar brain language discrete combinatorial rule neuronal assembly syntax correlation linguistics unsupervised learning