# Dynamics of Random Neural Networks with Bistable Units

arXiv: https://arxiv.org/abs/1409.2535 · HTML: https://mlsanity.com/paper/1409.2535/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2014.

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

- [Input correlations impede suppression of chaos and learning in balanced rate networks](https://mlsanity.com/paper/2201.09916/) (2022)
- [Decomposing heterogeneous dynamical systems with graph neural networks](https://mlsanity.com/paper/2407.19160/) (2024)
- [Dynamical stability for dense patterns in attractor neural networks](https://mlsanity.com/paper/2507.10383/) (2025)
- [Graph neural networks uncover structure and functions underlying the activity of simulated neural assemblies](https://mlsanity.com/paper/2602.13325/) (2026)

## Abstract (arXiv)

We construct and analyze a rate-based neural network model in which self-interacting units represent clusters of neurons with strong local connectivity and random inter-unit connections reflect long-range interactions. When sufficiently strong, the self-interactions make the individual units bistable. Simulation results, mean-field calculations and stability analysis reveal the different dynamic regimes of this network and identify the locations in parameter space of its phase transitions. We identify an interesting dynamical regime exhibiting transient but long-lived chaotic activity that combines features of chaotic and multiple fixed-point attractors.