ROSE: A Universal Neural Grammar

Processing natural language syntax requires a negotiation between symbolic and subsymbolic representations. Building on the recent representation, operation, structure, encoding (ROSE) neurocomputational architecture for syntax that scales from single units to inter-areal dynamics, I discuss the prospects of reconciling the neural code for hierarchical syntax with predictive processes. Here, the higher levels of ROSE provide instructions for symbolic phrase structure representations (S/E), while the lower levels provide probabilistic aspects of linguistic processing (R/O), with different types of cross-frequency coupling being hypothesized to interface these domains. I argue that ROSE provides a possible infrastructure for flexibly implementing distinct types of minimalist grammar parsers for the real-time processing of language. This perspective helps furnish a more restrictive 'core language network' in the brain than contemporary approaches that isolate general sentence composition. I define the language network as being critically involved in executing specific parsing operations (i.e. establishing phrasal categories, tree-structure depth, resolving dependencies, and retrieving proprietary lexical representations), capturing these network-defining operations jointly with probabilistic aspects of parsing. ROSE offers a 'mesoscopic protectorate' for natural language; an intermediate level of emergent organizational complexity that demands multi-scale modeling. By drawing principled relations across computational, algorithmic and implementational Marrian levels, ROSE offers new constraints on what a unified neurocomputational settlement for natural language syntax might look like, providing a tentative scaffold for a 'Universal Neural Grammar' - a species-specific format for neurally organizing the construction of compositional syntactic structures, which matures in accordance with a genetically determined biological matrix.