In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level. We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods. The ranked contribution of individual cells to the network's output is computed by analyzing a set of interpretable metrics of their decoupled step and sinusoidal responses. As a result, our method is able to uniquely identify neurons with insightful dynamics, quantify relationships between dynamical properties and test accuracy through ablation analysis, and interpret the impact of network capacity on a network's dynamical distribution. Finally, we demonstrate generalizability and scalability of our method by evaluating a series of different benchmark sequential datasets.
@article{arxiv.1809.03864,
title = {Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks},
author = {Ramin M. Hasani and Alexander Amini and Mathias Lechner and Felix Naser and Radu Grosu and Daniela Rus},
journal= {arXiv preprint arXiv:1809.03864},
year = {2018}
}