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Modeling reservoir computing with the discrete nonlinear Schrodinger equation
KTH, School of Engineering Sciences (SCI), Applied Physics, Materials and Nanophysics.
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. RISE SICS, Electrum 229, SE-16429 Kista, Sweden..ORCID iD: 0000-0001-7949-1815
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Applied Material Physics. KTH, Centres, SeRC - Swedish e-Science Research Centre.ORCID iD: 0000-0001-7788-6127
2018 (English)In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 98, no 5, article id 052101Article in journal (Refereed) Published
Abstract [en]

We formulate, using the discrete nonlinear Schrodinger equation (DNLS), a general approach to encode and process information based on reservoir computing. Reservoir computing is a promising avenue for realizing neuromorphic computing devices. In such computing systems, training is performed only at the output level by adjusting the output from the reservoir with respect to a target signal. In our formulation, the reservoir can be an arbitrary physical system, driven out of thermal equilibrium by an external driving. The DNLS is a general oscillator model with broad application in physics, and we argue that our approach is completely general and does not depend on the physical realization of the reservoir. The driving, which encodes the object to be recognized, acts as a thermodynamic force, one for each node in the reservoir. Currents associated with these thermodynamic forces in turn encode the output signal from the reservoir. As an example, we consider numerically the problem of supervised learning for pattern recognition, using as a reservoir a network of nonlinear oscillators.

Place, publisher, year, edition, pages
AMER PHYSICAL SOC , 2018. Vol. 98, no 5, article id 052101
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-239083DOI: 10.1103/PhysRevE.98.052101ISI: 000448929900001Scopus ID: 2-s2.0-85056391374OAI: oai:DiVA.org:kth-239083DiVA, id: diva2:1264905
Funder
Swedish Energy Agency, STEM P40147-1Swedish Research Council, VR 2016-05980Swedish Research Council, VR 2016-01961Swedish Research Council, VR 2015-04608
Note

QC 20181121

Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2019-08-20Bibliographically approved

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Borlenghi, SimoneBoman, MagnusDelin, Anna

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