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.
CITATION STYLE
Dale, M., Miller, J. F., Stepney, S., & Trefzer, M. A. (2016). Evolving carbon nanotube reservoir computers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9726, pp. 49–61). Springer Verlag. https://doi.org/10.1007/978-3-319-41312-9_5
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