Reservoir computing with swarms

We study swarms as dynamical systems for reservoir computing (RC). By example of a modified Reynolds boids model, the specific symmetries and dynamical properties of a swarm are explored with respect to a nonlinear time-series prediction task. Specifically, we seek to extract meaningful information...

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Bibliographic Details
Published in:Chaos (Woodbury, N.Y.) N.Y.), 2021-03, Vol.31 (3), p.033121-033121
Main Authors: Lymburn, Thomas, Algar, Shannon D., Small, Michael, Jüngling, Thomas
Format: Article
Language:English
Subjects:
Computation
Order parameters
Permutations
Phase transitions
Substrates
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Summary:We study swarms as dynamical systems for reservoir computing (RC). By example of a modified Reynolds boids model, the specific symmetries and dynamical properties of a swarm are explored with respect to a nonlinear time-series prediction task. Specifically, we seek to extract meaningful information about a predator-like driving signal from the swarm’s response to that signal. We find that the naïve implementation of a swarm for computation is very inefficient, as permutation symmetry of the individual agents reduces the computational capacity. To circumvent this, we distinguish between the computational substrate of the swarm and a separate observation layer, in which the swarm’s response is measured for use in the task. We demonstrate the implementation of a radial basis-localized observation layer for this task. The behavior of the swarm is characterized by order parameters and measures of consistency and related to the performance of the swarm as a reservoir. The relationship between RC performance and swarm behavior demonstrates that optimal computational properties are obtained near a phase transition regime.
ISSN:1054-1500
1089-7682
DOI:10.1063/5.0039745