Characterization and modeling of spiking and bursting in experimental NbOx neuron

Marie Drouhin*, Shuai Li, Matthieu Grelier, Sophie Collin, Florian Godel, Robert G. Elliman, Bruno Dlubak, Juan Trastoy, Damien Querlioz, Julie Grollier

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Hardware spiking neural networks hold the promise of realizing artificial intelligence with high energy efficiency. In this context, solid-state and scalable memristors can be used to mimic biological neuron characteristics. However, these devices show limited neuronal behaviors and have to be integrated in more complex circuits to implement the rich dynamics of biological neurons. Here we studied a NbOx memristor neuron that is capable of emulating numerous neuronal dynamics, including tonic spiking, stochastic spiking, leaky-integrate-and-fire features, spike latency, temporal integration. The device also exhibits phasic bursting, a property that has scarcely been observed and studied in solid-state nano-neurons. We show that we can reproduce and understand this particular response through simulations using non-linear dynamics. These results show that a single NbOx device is sufficient to emulate a collection of rich neuronal dynamics that paves a path forward for realizing scalable and energy-efficient neuromorphic computing paradigms.

    Original languageEnglish
    Article number044008
    JournalNeuromorphic Computing and Engineering
    Volume2
    Issue number4
    DOIs
    Publication statusPublished - 1 Dec 2022

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