Research in substrate-based computing has shown that materials contain rich properties that can be exploited to solve computational problems. One such technique known as Evolution-in-materio uses evolutionary algorithms to manipulate material substrates for computation. However, in general, modelling the computational processes occurring in such systems is a difficult task and understanding what part of the embodied system is doing the computation is still fairly ill-defined. This chapter discusses the prospects of using Reservoir Computing as a model for in-materio computing, introducing new training techniques (taken from Reservoir Computing) that could overcome training difficulties found in the current Evolution-in-Materio technique.
CITATION STYLE
Dale, M., Miller, J. F., & Stepney, S. (2017). Advances in Unconventional Computing. Advances in Unconventional Computing: Volume 1: Theory, 22(October), 1–41. Retrieved from http://link.springer.com/10.1007/978-3-319-33924-5
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