By backpropagating through a mass-spring network, the authors co-design controller and body and observe that classification, memory, and nonlinearity are shared between brain and body.
Designing Chaotic Attractors: A Semi-supervised Approach
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abstract
Chaotic dynamics are ubiquitous in nature and useful in engineering, but their geometric design can be challenging. Here, we propose a method using reservoir computing to generate chaos with a desired shape by providing a periodic orbit as a template, called a skeleton. We exploit a bifurcation of the reservoir to intentionally induce unsuccessful training of the skeleton, revealing inherent chaos. The emergence of this untrained attractor, resulting from the interaction between the skeleton and the reservoir's intrinsic dynamics, offers a novel semi-supervised framework for designing chaos.
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Backpropagation through Soft Body: Investigating Information Processing in Brain-Body Coupling Systems
By backpropagating through a mass-spring network, the authors co-design controller and body and observe that classification, memory, and nonlinearity are shared between brain and body.