Zhang, X., Jing, X. and Feng, K. (2023) Optimal Combinatorial Neural Codes with Matched Metric δ_r: Characterization and Constructions. IEEE Transactions on Information Theory, 69, 5440-5448.
https://doi.org/10.1109/TIT.2023.3266010

has been cited by the following article:

• TITLE: Neural Codes Constructs Based on Combinatorial Design

  AUTHORS: Jin Huang

  KEYWORDS: Combinatorial Neural Codes, Orthogonal Latin Rectangle, Steiner System, Group Divisible Design, Transversal Design

  JOURNAL NAME: Applied Mathematics, Vol.16 No.1, January 23, 2025

  ABSTRACT: Neuroscience (also known as neurobiology) is a science that studies the structure, function, development, pharmacology and pathology of the nervous system. In recent years, C. Cotardo has introduced coding theory into neuroscience, proposing the concept of combinatorial neural codes. And it was further studied in depth using algebraic methods by C. Curto. In this paper, we construct a class of combinatorial neural codes with special properties based on classical combinatorial structures such as orthogonal Latin rectangle, disjoint Steiner systems, groupable designs and transversal designs. These neural codes have significant weight distribution properties and large minimum distances, and are thus valuable for potential applications in information representation and neuroscience. This study provides new ideas for the construction method and property analysis of combinatorial neural codes, and enriches the study of algebraic coding theory.