Structure from rank: Rank-order coding as a bridge from sequence to structure

TL;DR

This paper introduces a neural network model based on rank-order coding that efficiently encodes and reconstructs structured sequences, demonstrating robustness and sensitivity to hierarchical structure, bridging the gap from acoustic input to motor plans.

Contribution

The study presents a novel rank-order based neural network model that captures hierarchical structure and generalization in sequence processing, inspired by neural pathways involved in speech.

Findings

Efficient compression and reconstruction of utterances from partial cues.

Emergent structure-sensitive generation reflecting context-general sensorimotor representations.

Global novelty detection similar to the P3B wave, indicating hierarchical sequence sensitivity.

Abstract

Understanding how structured sequence information can be represented and generalized in neural systems is key to modeling the transition from acoustic input to emergent structure. In this study, we propose a rank-order based neural network inspired by the STG-LIFG-PMC pathway, modeling the bottom-up transition from acoustic input to abstract rank representation and the top-down generation from that representation to motor execution. Building on previous work in rank coding, we first demonstrate that this model efficiently compresses input while retaining the capacity to reconstruct full utterances from partial cues, revealing emergent structure-sensitive generation process that reflects context-general representations of sensorimotor states, which are later shaped into context-specific motor plans during speech planning. We then show that the network exhibits global-level novelty detection similar to the P3B novelty wave, replicating the global-sequence-sensitive mechanism. As a supplement, we also compare the model's behavior under local (index-level) and global (rank-level) perturbations, revealing robustness to superficial variation and sensitivity to abstract structural violation, key features associated with hierarchical generalization. These results suggest that rank-order coding not only serves as a compact encoding scheme but also captures hierarchical structure in acoustic sequences.