There is an aspect of high-dimensional chaos called chaotic itinerancy (CI) which can explain brain activity during memory recall and association,

Researchers and roboticists are continually trying to achieve autonomous functions in robots, and they often look toward the animal brain as a point of inspiration for control mechanisms. Because of the task-specific nature of robotic behavior, due to the reliance on predefined modules and control methodologies, they are often limited in flexibility.The newest development in this area is coming out of the University of Tokyo, where researchers have created an alternative machine learning-based method to give robotic AI spontaneous behaviors. The team did this by relying on intricate temporal patterns, such as an animal brain's neural activities.The research was published in Science Advances, titled "Designing spontaneous behavioral switching via chaotic itinerancy." High-Dimensional ChaosA dynamical system is a mathematical model of the ever-changing internal states of something, which describes robots and their control software. Researchers are especially focused on high-dimensional chaos, a class of dynamical systems, due to its impressive ability to model animal brains.