Multifunctional reservoir computing

Whereas the power of reservoir computing (RC) in inferring chaotic systems has been well established in the literature, the studies are mostly restricted to monofunctional machines where the training and testing data are acquired from the same attractor. Here, using the strategies of attractor labeling and trajectory separation, we propose a scheme of RC capable of learning multiple attractors generated by entirely different dynamics, namely multifunctional RC. Specifically, we demonstrate that by incorporating a label channel into the standard reservoir computer, a single machine is able to learn from data the dynamics of multiple chaotic attractors, while each attractor can be accurately retrieved by inputting just a scalar in the prediction phase. The dependence of the machine performance on the labeling and separation parameters is investigated, and it is found that the machine performance is optimized when the parameters take intermediate values. The working mechanism of multifunctional RC is analyzed by the method of functional networks in neuroscience, and it is revealed that each attractor is represented by a stable, unique functional network in the reservoir, and the optimal performance arises as a balance between the stability, complexity, and distinguishability of the functional networks.