AI RESEARCH
Negative Binomial Variational Autoencoders for Overdispersed Latent Modeling
arXiv CS.LG
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ArXi:2508.05423v2 Announce Type: replace Although artificial neural networks are often described as brain-inspired, their representations typically rely on continuous activations, such as the continuous latent variables in variational autoencoders (VAEs), which limits their biological plausibility compared to the discrete spike-based signaling in real neurons. Extensions like the Poisson VAE