  Variational autoencoders (VAE) employ Bayesian inference to interpret sensory
inputs, mirroring processes that occur in primate vision across both ventral
(Higgins et al., 2021) and dorsal (Vafaii et al., 2023) pathways. Despite their
success, traditional VAEs rely on continuous latent variables, which deviates
sharply from the discrete nature of biological neurons. Here, we developed the
Poisson VAE (P-VAE), a novel architecture that combines principles of
predictive coding with a VAE that encodes inputs into discrete spike counts.
Combining Poisson-distributed latent variables with predictive coding
introduces a metabolic cost term in the model loss function, suggesting a
relationship with sparse coding which we verify empirically. Additionally, we
analyze the geometry of learned representations, contrasting the P-VAE to
alternative VAE models. We find that the P-VAEencodes its inputs in relatively
higher dimensions, facilitating linear separability of categories in a
downstream classification task with a much better (5x) sample efficiency. Our
work provides an interpretable computational framework to study brain-like
sensory processing and paves the way for a deeper understanding of perception
as an inferential process.
