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

The paper introduces an algebraic framework that links neural networks to neural ideals, providing algorithms for computing and approximating these ideals. It demonstrates how to identify and interpret the features captured by each hidden‑layer neuron, validated on the MNIST dataset. An interactive software tool is released to visualize these neuron‑specific features.

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