Neural Ideals and Neural Codes: An Algebraic Framework for Neural Network Classification and Feature Interpretation

Abstract:Understanding the features captured by the hidden layers of neural networks is a fundamental challenge in machine learning, despite their widespread success across various classification problems. In this work, we propose an algebraic framework for examining neural networks that model classification problems. Certain results, such as the correspondence between the neural network and neural ideals, algorithms for computing the neural ideals, and a stabilization theorem that enables approximation of the neural ideals, are first established. As an application to the framework, we present algorithms to identify and interpret the features captured by each hidden-layer neuron. Along with these theoretical developments, the practical performance has been demonstrated on the MNIST digit dataset, and the results highlight the pivotal role of neural ideals as a mathematical and computational tool for analyzing the features captured by neural networks. Further, we develop an interactive software that builds on the presented framework to visualize the features captured by each neuron. This tool is available at this https URL

Machine Learning (cs.LG); Commutative Algebra (math.AC)

arXiv:2609.30279 [cs.LG]

(or arXiv:2609.30279v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.30279 Focus to learn more arXiv-issued DOI via DataCite

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