# How does the brain compute with probabilities?

arXiv: https://arxiv.org/abs/2409.02709 · HTML: https://mlsanity.com/paper/2409.02709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 162 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [A neurally plausible model learns successor representations in partially observable environments](https://mlsanity.com/paper/1906.09480/) (2019)
- [The implications of perception as probabilistic inference for correlated neural variability during behavior](https://mlsanity.com/paper/1409.0257/) (2014)
- [Flexible and accurate inference and learning for deep generative models](https://mlsanity.com/paper/1805.11051/) (2018)
- [The Hamiltonian brain: efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics](https://mlsanity.com/paper/1407.0973/) (2014)
- [A Philosophical Understanding of Representation for Neuroscience](https://mlsanity.com/paper/2102.06592/) (2021)
- [A probabilistic population code based on neural samples](https://mlsanity.com/paper/1811.09739/) (2018)
- [How does the primate brain combine generative and discriminative computations in vision?](https://mlsanity.com/paper/2401.06005/) (2024)
- [Inference by Reparameterization in Neural Population Codes](https://mlsanity.com/paper/1605.06544/) (2016)
- [Interpolating between sampling and variational inference with infinite stochastic mixtures](https://mlsanity.com/paper/2110.09618/) (2021)
- [Efficient Probabilistic Inference in Generic Neural Networks Trained with Non-Probabilistic Feedback](https://mlsanity.com/paper/1601.03060/) (2016)
- [Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains](https://mlsanity.com/paper/1804.09739/) (2018)
- [Clarifying the conceptual dimensions of representation in neuroscience](https://mlsanity.com/paper/2403.14046/) (2024)

## Built on by (most cited first, 1 of 1)

- [Graceful forgetting: Memory as a process](https://mlsanity.com/paper/2502.11105/) (2025)

## Abstract (arXiv)

This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question `How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations -- Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) -- and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and experiments.