ArticleScience advances2020
Designing spontaneous behavioral switching via chaotic itinerancy.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 53 citations in OpenAlex.
- Noise-Limited Failure of OGY Chaos Control in Regulating Monosynaptic Reflex Variability in the In Vivo Cat Spinal Cord.NeuroSci · 2026Article
- Energy landscapes and synergetic state transitions in frustrated Stuart-Landau oscillator networks: a homotopy continuation study.Frontiers in network physiology · 2026Article
- Embedding Bifurcations into Pneumatic Artificial Muscle.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Reservoir-computing based associative memory and itinerancy for complex dynamical attractors.Nature communications · 2024Article
- Reservoir computing using self-sustained oscillations in a locally connected neural network.Scientific reports · 2023Article
- Controlling chaotic itinerancy in laser dynamics for reinforcement learning.Science advances · 2022Article
- Emergence of sensory attenuation based upon the free-energy principle.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chaotic itinerancy is a frequently observed phenomenon in high-dimensional nonlinear dynamical systems and is characterized by itinerant transitions among multiple quasi-attractors. Several studies have pointed out that high-dimensional activity in animal brains can be observed to exhibit chaotic itinerancy, which is considered to play a critical role in the spontaneous behavior generation of animals. Thus, how to design desired chaotic itinerancy is a topic of great interest, particularly for neurorobotics researchers who wish to understand and implement autonomous behavioral controls. However, it is generally difficult to gain control over high-dimensional nonlinear dynamical systems. In this study, we propose a method for implementing chaotic itinerancy reproducibly in a high-dimensional chaotic neural network. 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 using the intrinsic high-dimensional chaos.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.