  Deep learning methods have recently been used to construct non-linear codes
for the additive white Gaussian noise (AWGN) channel with feedback. However,
there is limited understanding of how these black-box-like codes with many
learned parameters use feedback. This study aims to uncover the fundamental
principles underlying the first deep-learned feedback code, known as Deepcode,
which is based on an RNN architecture. Our interpretable model based on
Deepcode is built by analyzing the influence length of inputs and approximating
the non-linear dynamics of the original black-box RNN encoder. Numerical
experiments demonstrate that our interpretable model -- which includes both an
encoder and a decoder -- achieves comparable performance to Deepcode while
offering an interpretation of how it employs feedback for error correction.
