@inbook {994,
	title = {Neurosymbolic Hidden Neuron Analysis in Convolutional Neural Networks},
	booktitle = {Neuro-Symbolic AI: Bridging the Gap Between Neural Networks and Symbolic Reasoning},
	year = {2026},
	publisher = {Elsevier},
	organization = {Elsevier},
	chapter = {8},
	abstract = {<p class="rtejustify">This tutorial introduces a step-by-step, deductive pipeline for making the inner workings of neural networks more transparent by assigning human-understandable concepts to hidden neuron activations. The approach automatically maps neuron behavior to symbolic concepts drawn from structured knowledge sources and attaches an error margin to each label, providing a measure of confidence in its precision. While demonstrated in detail on the ADE20k scene dataset---including single-concept neurons, multiple neurons contributing to the same concept, and multi-concept neurons---the method is also applied to the SUN2012 dataset and adapted for a text classification task, highlighting its generalizability across modalities. The chapter is designed to be practical and educational, focusing on a replicable methodology that readers can adapt to varied applications. Through worked examples, visualizations, and evaluation strategies, the tutorial offers a clear, reusable framework for concept-based neuron analysis in both vision and language models.</p>
},
	author = {Abhilekha Dalal and Moumita Sen Sarma and Avishek Das and Samatha E. Akkamahadevi and Eugene Y. Vasserman and Pascal Hitzler}
}
