Topic: auxiliary neurons

It has been proposed that the visual cortex performs sampling-based probabilistic inference where neural responses represent stochastic samples from a posterior distribution. However, the highly-correlated nature of neural activity across time presents a computational ceiling on the effective sample size for any given time interval. Here, we show that auxiliary neurons, when implemented strategically, can speed up sampling rates in neural circuits to efficiently express an inferred posterior distribution. We train stabilized supralinear networks using a loss function that intrinsically rewards