  Advances in neural recording present increasing opportunities to study neural
activity in unprecedented detail. Latent variable models (LVMs) are promising
tools for analyzing this rich activity across diverse neural systems and
behaviors, as LVMs do not depend on known relationships between the activity
and external experimental variables. However, progress with LVMs for neuronal
population activity is currently impeded by a lack of standardization,
resulting in methods being developed and compared in an ad hoc manner. To
coordinate these modeling efforts, we introduce a benchmark suite for latent
variable modeling of neural population activity. We curate four datasets of
neural spiking activity from cognitive, sensory, and motor areas to promote
models that apply to the wide variety of activity seen across these areas. We
identify unsupervised evaluation as a common framework for evaluating models
across datasets, and apply several baselines that demonstrate benchmark
diversity. We release this benchmark through EvalAI.
http://neurallatents.github.io
