Characterization and modeling of spiking and bursting in experimental NbOx neuron. Issue 4 (1st December 2022)

• Record Type: Journal Article
• Authors: Drouhin, Marie; Li, Shuai; Grelier, Matthieu; Collin, Sophie; Godel, Florian; Elliman, Robert G; Dlubak, Bruno; Trastoy, Juan; Querlioz, Damien; Grollier, Julie
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 4(2022)
• Publication Date: 2022-12-01
• Subjects: spiking neuron -- nanoneuron -- memristor -- niobium oxide -- Poole–Frenkel transport; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac969a
• Languages: English
• ISSNs: 2634-4386

Abstract

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 NbO x 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 NbO x 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.