Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity

An emerging framework in neuroscience uses the rules that govern how a neural circuit's state evolves over time to understand the circuit's underlying computation. While these \textit{neural dynamics} cannot be directly measured, new techniques attempt to estimate them by modeling observed neural recordings as a low-dimensional latent dynamical system embedded into a higher-dimensional neural space. How these models represent the readout from latent space to neural space can affect the interpretability of the latent representation -- for example, for models with a linear readout could make simple, low-dimensional dynamics unfolding on a non-linear neural manifold appear excessively complex and high-dimensional. Additionally, standard readouts (both linear and non-linear) often lack injectivity, meaning that they don't obligate changes in latent state to directly affect activity in the neural space. During training, non-injective readouts incentivize the model to invent dynamics that misrepresent the underlying system and computation. To address the challenges presented by non-linearity and non-injectivity, we combined a custom readout with a previously developed low-dimensional latent dynamics model to create the Ordinary Differential equations autoencoder with Injective Nonlinear readout (ODIN). We generated a synthetic spiking dataset by non-linearly embedding activity from a low-dimensional dynamical system into higher-D neural activity. We show that, in contrast to alternative models, ODIN is able to recover ground-truth latent activity from these data even when the nature of the system and embedding are unknown. Additionally, we show that ODIN enables the unsupervised recovery of underlying dynamical features (e.g., fixed points) and embedding geometry (e.g., the neural manifold) over alternative models. Overall, ODIN's ability to recover ground-truth latent features with low dimensionality make it a promising method for distilling interpretable dynamics that can explain neural computation.