A Substrate-Independent Framework to Characterise Reservoir Computers

Research output: Working paper

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A Substrate-Independent Framework to Characterise Reservoir Computers. / Dale, Matthew; Miller, Julian F.; Stepney, Susan; Trefzer, Martin A.

arXiv, 2018.

Research output: Working paper

Harvard

Dale, M, Miller, JF, Stepney, S & Trefzer, MA 2018 'A Substrate-Independent Framework to Characterise Reservoir Computers' arXiv. <https://arxiv.org/abs/1810.07135>

APA

Dale, M., Miller, J. F., Stepney, S., & Trefzer, M. A. (2018). A Substrate-Independent Framework to Characterise Reservoir Computers. arXiv. https://arxiv.org/abs/1810.07135

Vancouver

Dale M, Miller JF, Stepney S, Trefzer MA. A Substrate-Independent Framework to Characterise Reservoir Computers. arXiv. 2018 Oct 16.

Author

Dale, Matthew ; Miller, Julian F. ; Stepney, Susan ; Trefzer, Martin A. / A Substrate-Independent Framework to Characterise Reservoir Computers. arXiv, 2018.

Bibtex - Download

@techreport{1ec57210b0e7418f95678de411b8fb32,
title = "A Substrate-Independent Framework to Characterise Reservoir Computers",
abstract = " The Reservoir Computing (RC) framework states that any non-linear, input-driven dynamical system (the reservoir) exhibiting properties such as a fading memory and input separability can be trained to perform computational tasks. This broad inclusion of systems has led to many new physical substrates for RC. Properties essential for reservoirs to compute are tuned through reconfiguration of the substrate, such as change in virtual topology or physical morphology. As a result, each substrate possesses a unique {"}quality{"} --- obtained through reconfiguration --- to realise different reservoirs for different tasks. Here we describe an experimental framework that can be used to characterise the quality of any substrate for RC. Our framework reveals that a definition of quality is not only useful to compare substrates, but can also help map the non-trivial relationship between properties and task performance. And through quality, we may even be able to predict the performance of similarly behaved substrates. Applying the framework, we can explain why a previously investigated carbon nanotube/polymer composite performs modestly on tasks, due to a poor quality. In the wider context, the framework offers a greater understanding to what makes a dynamical system compute, helping improve the design of future substrates for RC. ",
keywords = "cs.ET",
author = "Matthew Dale and Miller, {Julian F.} and Susan Stepney and Trefzer, {Martin A.}",
year = "2018",
month = oct,
day = "16",
language = "English",
publisher = "arXiv",
type = "WorkingPaper",
institution = "arXiv",

}

RIS (suitable for import to EndNote) - Download

TY - UNPB

T1 - A Substrate-Independent Framework to Characterise Reservoir Computers

AU - Dale, Matthew

AU - Miller, Julian F.

AU - Stepney, Susan

AU - Trefzer, Martin A.

PY - 2018/10/16

Y1 - 2018/10/16

N2 - The Reservoir Computing (RC) framework states that any non-linear, input-driven dynamical system (the reservoir) exhibiting properties such as a fading memory and input separability can be trained to perform computational tasks. This broad inclusion of systems has led to many new physical substrates for RC. Properties essential for reservoirs to compute are tuned through reconfiguration of the substrate, such as change in virtual topology or physical morphology. As a result, each substrate possesses a unique "quality" --- obtained through reconfiguration --- to realise different reservoirs for different tasks. Here we describe an experimental framework that can be used to characterise the quality of any substrate for RC. Our framework reveals that a definition of quality is not only useful to compare substrates, but can also help map the non-trivial relationship between properties and task performance. And through quality, we may even be able to predict the performance of similarly behaved substrates. Applying the framework, we can explain why a previously investigated carbon nanotube/polymer composite performs modestly on tasks, due to a poor quality. In the wider context, the framework offers a greater understanding to what makes a dynamical system compute, helping improve the design of future substrates for RC.

AB - The Reservoir Computing (RC) framework states that any non-linear, input-driven dynamical system (the reservoir) exhibiting properties such as a fading memory and input separability can be trained to perform computational tasks. This broad inclusion of systems has led to many new physical substrates for RC. Properties essential for reservoirs to compute are tuned through reconfiguration of the substrate, such as change in virtual topology or physical morphology. As a result, each substrate possesses a unique "quality" --- obtained through reconfiguration --- to realise different reservoirs for different tasks. Here we describe an experimental framework that can be used to characterise the quality of any substrate for RC. Our framework reveals that a definition of quality is not only useful to compare substrates, but can also help map the non-trivial relationship between properties and task performance. And through quality, we may even be able to predict the performance of similarly behaved substrates. Applying the framework, we can explain why a previously investigated carbon nanotube/polymer composite performs modestly on tasks, due to a poor quality. In the wider context, the framework offers a greater understanding to what makes a dynamical system compute, helping improve the design of future substrates for RC.

KW - cs.ET

M3 - Working paper

BT - A Substrate-Independent Framework to Characterise Reservoir Computers

PB - arXiv

ER -