Integrating deep neural networks (DNNs) into wireless receivers can enhance reliability in the presence of hard-to-model channels. However, in order to successfully deploy deep receivers, one must address the rapid channel variations while accounting for the limited availability of data and computing resources. This paper presents a novel framework for rapid online learning of deep receivers that builds on continual Bayesian learning. By modeling the channel variations as a dynamic system in the space of DNN model parameters, we enable efficient single-step updates, supporting the rapid training of Bayesian DNNs using limited data. We propose two online learning algorithms based on extended Kalman filtering and on Bayesian gradients. Unlike typical approaches that avoid catastrophic forgetting, our methods prioritize adapting to current channel realization. Numerical results show that the proposed continual Bayesian learning formulation yields deep receivers that can effectively adapt to varying channels with minimal computational overhead.

White matter tractography is an advanced neuroimaging technique that reconstructs the 3D white matter pathways of the brain from diffusion MRI data. It can be framed as a pathfinding problem aiming to infer neural fiber trajectories from noisy and ambiguous measurements, facing challenges such as crossing, merging, and fanning white-matter configurations. In this paper, we propose a novel tractography method that leverages Transformers to model the sequential nature of white matter streamlines, enabling the prediction of fiber directions by integrating both the trajectory context and current diffusion MRI measurements. To incorporate spatial information, we utilize CNNs that extract microstructural features from local neighborhoods around each voxel. By combining these complementary sources of information, our approach improves the precision and completeness of neural pathway mapping compared to traditional tractography models. We evaluate our method with the Tractometer toolkit, achieving competitive performance against state-of-the-art approaches, and present qualitative results on the TractoInferno dataset, demonstrating strong generalization to real-world data. Our code is publicly available at https://github.com/ItzikWaizman/TractoTransformer.