Abstract
Reservoir Computing is a useful general theoretical model for many dynamical systems. Here we show the first steps to applying the reservoir model as a simple computational layer to extract exploitable information from physical substrates consisting of single-walled carbon nanotubes and polymer mixtures. We argue that many physical substrates can be represented and configured into working reservoirs given some pre-training through evolutionary selected input-output mappings and targeted input stimuli.
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Acknowledgments
This work was funded by a Defence Science and Technology Laboratory (DSTL) PhD studentship.
The authors thank the EU NASCENCE Project (http://www.nascence.eu) for providing the SWCNT materials used in this work.
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Dale, M., Miller, J.F., Stepney, S., Trefzer, M.A. (2016). Evolving Carbon Nanotube Reservoir Computers. In: Amos, M., CONDON, A. (eds) Unconventional Computation and Natural Computation. UCNC 2016. Lecture Notes in Computer Science(), vol 9726. Springer, Cham. https://doi.org/10.1007/978-3-319-41312-9_5
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DOI: https://doi.org/10.1007/978-3-319-41312-9_5
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