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

arXiv: https://arxiv.org/abs/2210.01691 · HTML: https://mlsanity.com/paper/2210.01691/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Concrete Dropout](https://mlsanity.com/paper/1705.07832/) (2017)
- [Survey of Dropout Methods for Deep Neural Networks](https://mlsanity.com/paper/1904.13310/) (2019)
- [Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines](https://mlsanity.com/paper/1511.04484/) (2015)
- [A learning framework for winner-take-all networks with stochastic synapses](https://mlsanity.com/paper/1708.04251/) (2017)
- [Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations](https://mlsanity.com/paper/1505.04544/) (2015)

## Abstract (arXiv)

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.