Coarse--graining, fixed points, and scaling in a large population of neurons
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2018.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 2.
4 papers.
- 2023Bayesian Renormalizationcited by 13
- 2025Data coarse graining can improve model performancecited by 0
- 2026Neurodata Without Boredom: Benchmarking Agentic AI for Data Reusecited by 0
- 2026Learning reshapes power-law anisotropy in internal representationscited by 0
- 2026Learning reshapes power-law anisotropy in internal representations15 Aug 2026
- 2026Neurodata Without Boredom: Benchmarking Agentic AI for Data Reuse12 May 2026
- 2025Data coarse graining can improve model performance18 Sep 2025
- 2023Bayesian Renormalization17 May 2023