Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain

TL;DR

This paper shows that adaptive synaptic failure in the brain can enable sampling from posterior predictive distributions, providing a potential neural mechanism for probabilistic inference and complex calculations.

Contribution

It introduces a novel model where adaptive synaptic failure enables sampling from posterior predictive distributions, extending previous models that only sampled model uncertainty.

Findings

Synaptic failure can sample from posterior predictive distributions.

Adaptive transmission probabilities improve probabilistic inference.

The model explains brain's ability for probabilistic searches and complex integrations.

Abstract

Bayesian interpretations of neural processing require that biological mechanisms represent and operate upon probability distributions in accordance with Bayes' theorem. Many have speculated that synaptic failure constitutes a mechanism of variational, i.e., approximate, Bayesian inference in the brain. Whereas models have previously used synaptic failure to sample over uncertainty in model parameters, we demonstrate that by adapting transmission probabilities to learned network weights, synaptic failure can sample not only over model uncertainty, but complete posterior predictive distributions as well. Our results potentially explain the brain's ability to perform probabilistic searches and to approximate complex integrals. These operations are involved in numerous calculations, including likelihood evaluation and state value estimation for complex planning.