# Designing spontaneous behavioral switching via chaotic itinerancy

arXiv: https://arxiv.org/abs/2002.08332 · HTML: https://mlsanity.com/paper/2002.08332/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [Seeing double with a multifunctional reservoir computer](https://mlsanity.com/paper/2305.05799/) (2023)
- [Controlling chaotic itinerancy in laser dynamics for reinforcement learning](https://mlsanity.com/paper/2205.05987/) (2022)
- [Learning Reservoir Dynamics with Temporal Self-Modulation](https://mlsanity.com/paper/2301.09235/) (2023)
- [Attention-Enhanced Reservoir Computing](https://mlsanity.com/paper/2312.16503/) (2023)
- [Designing Chaotic Attractors: A Semi-supervised Approach](https://mlsanity.com/paper/2407.09545/) (2024)
- [Bidirectional Interaction between Visual and Motor Generative Models using Predictive Coding and Active Inference](https://mlsanity.com/paper/2104.09163/) (2021)
- [Transient Chaos in BERT](https://mlsanity.com/paper/2106.03181/) (2021)
- [Multifunctional physical reservoir computing in soft tensegrity robots](https://mlsanity.com/paper/2507.21496/) (2025)
- [A Predictive Coding Account for Chaotic Itinerancy](https://mlsanity.com/paper/2106.08937/) (2021)
- [Emergence of sensory attenuation based upon the free-energy principle](https://mlsanity.com/paper/2111.02666/) (2021)
- [Composite FORCE learning of chaotic echo state networks for time-series prediction](https://mlsanity.com/paper/2207.02420/) (2022)
- [Attractor-merging Crises and Intermittency in Reservoir Computing](https://mlsanity.com/paper/2504.12695/) (2025)
- [Basin Geometry and Reliable Recall of Dynamical Memories in Reservoir Computing](https://mlsanity.com/paper/2609.01914/) (2026)

## Abstract (arXiv)

Chaotic itinerancy is a frequently observed phenomenon in high-dimensional and nonlinear dynamical systems, and it is characterized by the random transitions among multiple quasi-attractors. Several studies have revealed that chaotic itinerancy has been observed in brain activity, and it is considered to play a critical role in the spontaneous, stable behavior generation of animals. Thus, chaotic itinerancy is a topic of great interest, particularly for neurorobotics researchers who wish to understand and implement autonomous behavioral controls for agents. However, it is generally difficult to gain control over high-dimensional nonlinear dynamical systems. Hence, the implementation of chaotic itinerancy has mainly been accomplished heuristically. In this study, we propose a novel way of implementing chaotic itinerancy reproducibly and at will in a generic high-dimensional chaotic system. In particular, we demonstrate that our method enables us to easily design both the trajectories of quasi-attractors and the transition rules among them simply by adjusting the limited number of system parameters and by utilizing the intrinsic high-dimensional chaos. Finally, we quantitatively discuss the validity and scope of application through the results of several numerical experiments.