In this paper, we apply the Polymerase-Exonuclease-Nickase Dynamic Network Assembly (PEN DNA) toolbox, a modular framework for molecular computing, to reservoir computing. While reservoir computing is traditionally implemented with recurrent neural networks, any system with similar recurrent properties, here chemical reaction networks (CRNs), can be used as a reservoir.

We compared our approach to a previous CRN implementation of reservoir computing by Goudarzi et al. Our implementation yielded similar performance with respect to their benchmark tasks.

We then took advantage of the modularity of the PEN DNA toolbox to investigate the impact of the CRN size on performance, both by hand and with an automated optimization process. In both cases, we were able to find systems with excellent performance while also being realistic with respect to in vitro implementation.

Finally, we investigated the impact of constraining the weights of the output layer to be positive. This constraint guarantees that the system will remain relatively small, and thus makes it easier to implement in vitro. While this constraint led to an expected degradation in performance, we were still able to find good implementations of the reservoir.

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