01698nas a2200169   4500008004100000245007400041210006900115260001300184520108200197100002101279700002401300700001701324700003001341700002601371700002001397856011101417       2026                        eng d00aNeurosymbolic Hidden Neuron Analysis in Convolutional Neural Networks0 aNeurosymbolic Hidden Neuron Analysis in Convolutional Neural Net  bElsevier3 a<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>
1 aDalal, Abhilekha1 aSarma, Moumita, Sen1 aDas, Avishek1 aAkkamahadevi, Samatha, E.1 aVasserman, Eugene, Y.1 aHitzler, Pascal  uhttps://daselab.cs.ksu.edu/publications/neurosymbolic-hidden-neuron-analysis-convolutional-neural-networks