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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 8: Machine Learning in Dynamics and Statistical Physics II (joint session DY/SOE)
SOE 8.5: Talk
Tuesday, March 19, 2024, 10:30–10:45, BH-N 243
Excitability and Memory in a Time-Delayed Optoelectronic Neuron — •Jonas Mayer Martins1, Svetlana V. Gurevich1, and Julien Javaloyes2 — 1Institute for Theoretical Physics, University of Münster, Wilhelm-Klemm-Str. 9 and Center for Nonlinear Science (CeNoS), University of Münster, Corrensstrasse 2, 48149 Münster, Germany — 2Departament de Física and IAC-3, Universitat de les Illes Balears, C/ Valldemossa km 7.5, 07122 Mallorca
We study the dynamics of an optoelectronic circuit composed of a nanoscale resonant-tunneling diode (RTD) in the excitable regime driving a nanolaser diode (LD) coupled via time-delayed feedback. Using a combination of numerical path-continuation methods and time simulations, we demonstrate that the RTD-LD system can serve as an artificial neuron, generating pulses in the form of temporal localized states (TLSs) that can be employed as memory for neuromorphic computing. In particular, our findings reveal that the prototypical delayed FitzHugh--Nagumo model previously employed to model the RTD-LD resembles our more realistic model qualitatively only in the limit of a slow RTD. We show that the RTD time scale plays a critical role in how the RTD-LD can be used as memory because it governs a shift in pulse interaction forces from repulsive to attractive, leading to a transition from stable to unstable multi-pulse TLSs. Our theoretical analysis uncovers novel features and challenges, including the multistability of TLSs and attractive interaction forces, stemming from the previously neglected intrinsic dynamics of the laser. These dynamics are crucial to consider for the memory properties of the RTD-LD.
Keywords: Neuromorphic computing; Delay; Excitability; Laser; Resonant-tunneling diode