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.
doi:10.1007/978-3-319-41312-9_5 | PDF
@inproceedings(Dale-UCNC-2016, author = "Matthew Dale and Julian F. Miller and Susan Stepney and Martin A. Trefzer", title = "Evolving Carbon Nanotube Reservoir Computers", pages = "49-61", crossref = "UCNC-2016" ) @proceedings(UCNC-2016, title = "UCNC 2016, Manchester, UK, July 2016", booktitle = "UCNC 2016, Manchester, UK, July 2016", series = "LNCS", volume = 9726, publisher = "Springer", year = 2016 )