\section{Discussion}
\label{sec:discussion}
Motivated by recent progress in learning generalizable NFs, we present \textbf{MedFuncta}, a unified framework for efficiently learning large-scale neural representations of medical signals. We not only introduce a novel $\omega$-schedule for commonly used SIRENs, effectively enforcing a staged optimization process through layer-wise learning rates, but also address the computational overhead of previous methods by presenting an efficient meta-learning framework that utilizes training with sparse supervision. We validate our findings through extensive experiments and ablations across a wide range of diverse medical signals and demonstrate that we can solve relevant downstream tasks on our proposed neural representation. While this work is still limited to rather low resolutions, we believe that working on such neural representations holds substantial long-term potential, especially for heterogeneous medical data. For future research, we identify two main directions: (1) further scaling the method by exploring different architectures, conditioning mechanisms, or more advanced training approaches such as sequential mini-batch processing in the inner loop or novel optimization strategies \cite{mcginnis2025optimizing}; (2) exploring further downstream tasks that benefit from generalizing NFs across a dataset. Possible applications include but are not limited to resolution-agnostic synthesis \cite{dupont2021generative}, image segmentation \cite{vyas2025fit}, registration \cite{wolterink2022implicit}, atlas building \cite{dannecker2024cina}, spatiotemporal growth modeling \cite{bieder2024modeling}, and various inverse problems like super-resolution \cite{mcginnis2023single}. We hope that \textbf{MedFuncta} inspires the community and serves as a foundation for future research into neural representations of medical data.