Optical coherence tomography (OCT), the standard clinical imaging procedure in ophthalmology, provides high-resolution cross-sectional images of the retina, but is usually performed with large slice distances. Small structures in the sparsely scanned retina can therefore be missed, and volumetric measurements are impaired. Interpolation methods that work with single images can generate densely sampled volumes, but fail to correctly interpolate shapes and cannot generate information that is missing between the given slices. In thiswork,we propose to use generalized implicit neural representations (INRs) for OCT interpolation and retinal layer segmentation. By using population-based training, the shape representation is improved over baselines, while the training requires only very few annotated image slices thanks to the ability of INRs to handle highly anisotropic data. To enable the integration of inter-slice information, we use additional SLO images, demonstrating a new way to combine different eye imaging modalities. Finally, it is shown that the generalized INR can be adapted to images that were not seen during training, enabling the segmentation of new images.