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

Xiaodan Chen¹, Alexandre Pitti¹, Mathias Quoy¹

¹ETIS, CY Cergy-Paris Université - ENSEA - CNRS, UMR 8051, 2 Av. Adolphe Chauvin, Pontoise, 95300, Ile de France, France; A*Star, 1 Fusionopolis Way, #20-10 Connexis North Tower, Singapore, 138632, Singapore; IPAL (International Research Laboratory on Artificial Intelligence), 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore, 138632, Singapore.

Neural Networks : the Official Journal of the International Neural Network Society

|March 18, 2026

Summary

This summary is machine-generated.

This study introduces a rank-order neural network that efficiently compresses acoustic input and reconstructs speech. The model demonstrates novelty detection and hierarchical generalization, suggesting rank-order coding captures acoustic sequence structure.

Keywords:

Abstract chunking Hierarchical structure Rank-order coding Structure processing

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Area of Science:

Computational Neuroscience
Cognitive Science
Speech Processing

Background:

Understanding neural representation of structured sequences is crucial for speech processing.
Existing models struggle to capture the transition from acoustic input to abstract representations.

Purpose of the Study:

To propose and evaluate a rank-order based neural network for modeling speech perception and production.
To investigate how rank-order coding represents and generalizes acoustic sequence information.

Main Methods:

Developed a novel neural network inspired by the STG-LIFG-PMC pathway.
Employed rank-order coding to compress acoustic input and generate motor plans.
Tested the model's response to local and global perturbations and novelty detection.

Main Results:

The model efficiently compresses input and reconstructs utterances from partial cues.
Demonstrated emergent structure-sensitive generation and context-general sensorimotor representations.
Exhibited global-level novelty detection similar to the P3B wave and robustness to superficial variations.

Conclusions:

Rank-order coding serves as a compact encoding scheme for acoustic sequences.
This coding method captures hierarchical structure and supports generalization in neural systems.
The proposed model offers insights into the neural mechanisms of speech planning and sequence processing.