We explore the application of neural fields for tomographic reconstructions of the solar corona using data from the Large Angle and Spectrometric Coronagraph (LASCO)-C2 instrument. We first demonstrate their ability to recover the electron-density volume in a synthetic static case, utilizing a simulated 3D model of the corona. Our results show that neural fields provide an efficient and accurate representation of the electron-density data. By comparing the synthesized polarized brightness from the modeled electron densities and the observations, we validate the performance of the method in recovering the electron-density structure accurately. Furthermore, we extend our analysis to the dynamic case, considering time-dependent reconstructions. To this end, we incorporate the temporal dimension into the neural field. The results demonstrate that neural fields can effectively capture the temporal variability of the coronal electron density. We apply the developed tomographic strategy to real observations from the LASCO-C2 instrument. Using a sequence of LASCO-C2 images, we reconstruct the electron-density distribution of the solar corona for a specific period. The reconstructed electron densities are able to reproduce the observed features of the polarized brightness with fidelity, although some streamers are not properly fitted. The results indicate that neural fields provide a powerful tool for tomographic reconstructions, yielding electron-density maps with minimal artifacts and improved agreement with observations. Neural fields offer several advantages, including efficient interpolation, easy to implement implicit and explicit regularization, and the ability to capture temporal variability. The proposed approach has the potential to enhance our understanding of the complex dynamics and structures of the solar corona, enabling more accurate and detailed analyses of coronal features.