@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}
}
@article {986,
	title = {Deep Deductive Reasoning is a Hard Deep Learning Problem},
	journal = {Neurosymbolic Artificial Intelligence},
	year = {2025},
	author = {Pascal Hitzler and Rushrukh Rayan and Joseph Zalewski and Sanaz Saki Norouzi and Aaron Eberhart and Eugene Y. Vasserman}
}
@conference {972,
	title = {On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis},
	booktitle = {18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024},
	year = {2024},
	publisher = {Springer },
	organization = {Springer },
	abstract = {<p>We introduce a novel model-agnostic post-hoc Explainable AI method that provides meaningful interpretations for hidden neuron activations in a Convolutional Neural Network. Our approach uses a Wikipedia-derived concept hierarchy with approx. 2 million classes as background knowledge, and deductive reasoning based Concept Induc- tion for explanation generation. Additionally, we explore and compare the capabilities of off-the-shelf pre-trained multimodal-based explainable methods. Our evaluation shows that our neurosymbolic method holds a competitive edge in both quantitative and qualitative aspects.</p>
},
	keywords = {CNN, Concept Induction, Explainable AI, LLM},
	author = {Abhilekha Dalal and Rushrukh Rayan and Adrita Barua and Eugene Y. Vasserman and Kamruzzaman Sarker and Pascal Hitzler}
}
@article {969,
	title = {Deep Deductive Reasoning is a Hard Deep Learning Problem},
	year = {2023},
	author = {Pascal Hitzler and Rushrukh Rayan and Joseph Zalewski and Sanaz Saki Norouzi and Aaron Eberhart and Eugene Y. Vasserman}
}
