@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}
}
@inbook {995,
	title = {Symbolic Reasoning Methods in Neural Architectures and Large Language Models},
	booktitle = {Neuro-Symbolic AI: Bridging the Gap Between Neural Networks and Symbolic Reasoning},
	year = {2026},
	chapter = {5},
	abstract = {<p>This chapter presents a comprehensive overview of contemporary approaches that integrate neural networks and large language models (LLMs) with classical symbolic reasoning. We review the evolution of methods designed to embed logical inference within neural architectures and explore recent advances in prompting strategies, hybrid reasoning frameworks, retrieval-augmented learning, and reinforcement-based reasoning optimization methods. We discuss the symbolic foundations of logical reasoning and then analyze how neural and LLM-based methods have progressively evolved to emulate, extend, and optimize symbolic reasoning across diverse tasks. Finally, we explore emerging neurosymbolic paradigms that unify neural and symbolic reasoning to achieve interpretable, scalable, and generalizable intelligence. Our analysis underscores the growing importance of neurosymbolic AI as a foundational direction for developing reliable and explainable reasoning systems.</p>
},
	author = {Adrita Barua and Avishek Das and Moumita Sen Sarma and Pascal Hitzler}
}
