  With the recent addition of Retrieval-Augmented Generation (RAG), the scope
and importance of Information Retrieval (IR) has expanded. As a result, the
importance of a deeper understanding of IR models also increases. However,
interpretability in IR remains under-explored, especially when it comes to the
models' inner mechanisms. In this paper, we explore the possibility of adapting
Integrated Gradient-based methods in an IR context to identify the role of
individual neurons within the model. In particular, we provide new insights
into the role of what we call "relevance" neurons, as well as how they deal
with unseen data. Finally, we carry out an in-depth pruning study to validate
our findings.
