  We consider the application of the factor graph framework for symbol
detection on linear inter-symbol interference channels. Based on the Ungerboeck
observation model, a detection algorithm with appealing complexity properties
can be derived. However, since the underlying factor graph contains cycles, the
sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we
develop and evaluate efficient strategies to improve the performance of the
factor graph-based symbol detection by means of neural enhancement. In
particular, we consider neural belief propagation and generalizations of the
factor nodes as an effective way to mitigate the effect of cycles within the
factor graph. By applying a generic preprocessor to the channel output, we
propose a simple technique to vary the underlying factor graph in every SPA
iteration. Using this dynamic factor graph transition, we intend to preserve
the extrinsic nature of the SPA messages which is otherwise impaired due to
cycles. Simulation results show that the proposed methods can massively improve
the detection performance, even approaching the maximum a posteriori
performance for various transmission scenarios, while preserving a complexity
which is linear in both the block length and the channel memory.
