Before revision:  We apply the method to several datasets and show that it is able to accurately infer the underlying dynamics of these systems , in some cases substantially outperforming state-of-the-art methods.
After revision:  We apply VIND to electrophysiology, single-cell voltage and widefield imaging datasets with state-of-the-art results in reconstruction error. In single-cell voltage data, VIND finds a 5D latent space, with variables akin to those of Hodgkin-Huxley-like models. VIND's learned dynamics are further quantified by predicting future neural activity. VIND excels in this task , in some cases substantially outperforming state-of-the-art methods.