Mar 20 – 22, 2019
Nordita, Stockholm
Europe/Stockholm timezone

Neuromorphic computing with off-equilibrium oscillator networks

Mar 21, 2019, 11:45 AM
132:028 (Nordita, Stockholm)


Nordita, Stockholm


Simone Borlenghi Garoia (KTH)


Oscillator networks are ubiquitous in Physics.Very different systems, From Bose-Einstein condensates to mechanical oscillators, spin systems and electrical power grids, can be described using the language of the discrete Nonlinear Schrödinger equation (DNLS). In this talk, I will show how the DNLS can be used to model a neural network for pattern recognition. An input is passed through the network, that reaches a off-equilibrium steady state and produces an output in the form of energy currents that flow between the oscillators. Different inputs are used to encode simple images (typically black and white digits), and the output is trained using standard machine learning techniques in order to discriminate between the various digits, with a recognition rate of more than 90%. This computational paradigm is called reservoir computing, where the term "reservoir" indicates any complex system able to perform information processing. The advantage of reservoir computing is that one can chose as network any nonlinear complex system, and the training is performed only at the output level, without modifying the network. This has huge advantages in terms of computational cost, so that a simple recognition problem can be performed on a laptop. The generality of the DNLS model suggests that a large class of microscopic and macroscopic systems can be used for this purpose, and the techinque employed is somewhat universal. References: Simone Borlenghi, Magnus Boman and Anna Delin, "Modelling reservoir computing with the discrete nonlinear Schrödinger equation", Phys. Rev. E 98, 052101 (2018) Stefano Iubini, Stefano Lepri and Antonio Politi, "Nonequilibrium discrete Nonlinear Schrödinger Equation", Phys. Rev. E, 86, 011108 (2012) ​Stefano Iubini, Stefano Lepri, Roberto Livi and Antonio Politi, "Off equilibrium Langevin dynamics of the discrete nonlinear Schrödinger chain", J. Stat. Mech, 2013 (2013) Mantas Lukosevicius and Herbert Jaeger, "Reservoir computing approaches to recurrent neural network training", Computer science reviews 3, 127 (2009)

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