# Coarse--graining, fixed points, and scaling in a large population of neurons

arXiv: https://arxiv.org/abs/1809.08461 · HTML: https://mlsanity.com/paper/1809.08461/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Bayesian Renormalization](https://mlsanity.com/paper/2305.10491/) (2023)
- [Data coarse graining can improve model performance](https://mlsanity.com/paper/2509.14498/) (2025)
- [Neurodata Without Boredom: Benchmarking Agentic AI for Data Reuse](https://mlsanity.com/paper/2605.12808/) (2026)
- [Learning reshapes power-law anisotropy in internal representations](https://mlsanity.com/paper/2608.15239/) (2026)

## Abstract (arXiv)

We develop a phenomenological coarse--graining procedure for activity in a large network of neurons, and apply this to recordings from a population of 1000+ cells in the hippocampus. Distributions of coarse--grained variables seem to approach a fixed non--Gaussian form, and we see evidence of scaling in both static and dynamic quantities. These results suggest that the collective behavior of the network is described by a non--trivial fixed point.