Voltage imaging is a powerful technique for observing fast neural activity, but it often produces images with a high level of noise, making analysis difficult. Deep learning methods have shown promise in denoising such data, but most require large datasets containing both clean and noisy image pairs, which are hard to obtain in real-world settings. To this end, several self-supervised approaches that rely solely on noisy images have been proposed in the literature. In this paper, three self-supervised denoising models, Noise2Void, AP-BSN, and DeepVID v2, are evaluated on both synthetic and real voltage imaging datasets. For the synthetic data, the performance is assessed using PSNR and SSIM, while for the real data, the temporal signal-to-noise ratio (tSNR), a metric well-suited to voltage imaging, is used. Results show that the self-supervised models are effective at denoising both synthetic and real image datasets. In particular, models which use the temporal information of the videos, such as DeepVID v2, obtain the best results.