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
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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
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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.
Figure 1: a Optical microscopy photograph, b layout view, and c schematic of the hybrid Memristor-CMOS die. d Electron microscopy image of a memristor in our hybrid memristor/CMOS process. e Measurement of memristor resistance as a function of number of RESET programming pulses, for implementing a synaptic learning rule. f Illustration of memristor programming states. g Schematic of the analog mode circuitry, with shift registers selecting inputs via Multiplexers . h Schematic of the digital mode circuitry, with a complementary 2T2R memristor basic cell. i Schematics of the sensing circuitry with XNOR logic-in-memory feature. j Schematic of the level shifters, used for shifting digital nominal voltage to forming and programming voltages of memristors. k Voltages applied for forming or programming a complementary cell in the digital mode. l Measurements setup of the prototyping platform. m Memristor endurance study, using the digital mode for programming and the analog mode for resistance measurements.
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