Binding events through the mutual synchronization of spintronic nano-neurons

Miguel Romera, Philippe Talatchian, Sumito Tsunegi, Kay Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Vincent Cros, Paolo Bortolotti, Maxence Ernoult, Damien Querlioz () and Julie Grollier ()
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Miguel Romera: Université Paris-Saclay
Philippe Talatchian: Université Paris-Saclay
Sumito Tsunegi: National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center
Kay Yakushiji: National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center
Akio Fukushima: National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center
Hitoshi Kubota: National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center
Shinji Yuasa: National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center
Vincent Cros: Université Paris-Saclay
Paolo Bortolotti: Université Paris-Saclay
Maxence Ernoult: Université Paris-Saclay
Damien Querlioz: Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies
Julie Grollier: Université Paris-Saclay

Nature Communications, 2022, vol. 13, issue 1, 1-7

Abstract: Abstract The brain naturally binds events from different sources in unique concepts. It is hypothesized that this process occurs through the transient mutual synchronization of neurons located in different regions of the brain when the stimulus is presented. This mechanism of ‘binding through synchronization’ can be directly implemented in neural networks composed of coupled oscillators. To do so, the oscillators must be able to mutually synchronize for the range of inputs corresponding to a single class, and otherwise remain desynchronized. Here we show that the outstanding ability of spintronic nano-oscillators to mutually synchronize and the possibility to precisely control the occurrence of mutual synchronization by tuning the oscillator frequencies over wide ranges allows pattern recognition. We demonstrate experimentally on a simple task that three spintronic nano-oscillators can bind consecutive events and thus recognize and distinguish temporal sequences. This work is a step forward in the construction of neural networks that exploit the non-linear dynamic properties of their components to perform brain-inspired computations.

Date: 2022
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DOI: 10.1038/s41467-022-28159-1

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