# Transition to chaos in random networks with cell-type-specific connectivity

arXiv: https://arxiv.org/abs/1407.2297 · HTML: https://mlsanity.com/paper/1407.2297/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2014.

## Built on by (most cited first, 6 of 6)

- [How connectivity structure shapes rich and lazy learning in neural circuits](https://mlsanity.com/paper/2310.08513/) (2023)
- [High-dimensional analysis of ridge regression for non-identically distributed data with a variance profile](https://mlsanity.com/paper/2403.20200/) (2024)
- [Input correlations impede suppression of chaos and learning in balanced rate networks](https://mlsanity.com/paper/2201.09916/) (2022)
- [Neural timescales from a computational perspective](https://mlsanity.com/paper/2409.02684/) (2024)
- [Extended critical regimes of deep neural networks](https://mlsanity.com/paper/2203.12967/) (2022)
- [Identifying Connectivity Distributions from Neural Dynamics Using Flows](https://mlsanity.com/paper/2603.26506/) (2026)

## Abstract (arXiv)

In neural circuits, statistical connectivity rules strongly depend on neuronal type. Here we study dynamics of neural networks with cell-type specific connectivity by extending the dynamic mean field method, and find that these networks exhibit a phase transition between silent and chaotic activity. By analyzing the locus of this transition, we derive a new result in random matrix theory: the spectral radius of a random connectivity matrix with block-structured variances. We apply our results to show how a small group of hyper-excitable neurons within the network can significantly increase the network's computational capacity.