Reservoir Computing as a Model for In-Materio Computing

  • Matthew Dale
  • Julian F. Miller
  • Susan Stepney
Part of the Emergence, Complexity and Computation book series (ECC, volume 22)


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.


Lyapunov Exponent Field Programmable Gate Array Kernel Quality Material Computation Recursive Little Square 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Matthew Dale is funded by a Defence Science and Technology Laboratory (DSTL) Ph.D. studentship.


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Matthew Dale
    • 1
  • Julian F. Miller
    • 2
  • Susan Stepney
    • 1
    • 3
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK
  2. 2.Department of ElectronicsUniversity of YorkYorkUK
  3. 3.York Centre for Complex Systems AnalysisUniversity of YorkYorkUK

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