DOI: https://doi.org/10.29363/nanoge.matnec.2022.005
Publication date: 23rd February 2022
A memristor is a two-terminal device that undergoes a voltage-controlled conductance change. Because the resistance depends on the history of the system, it has a strong hysteresis effect and produces a resistance switching. Memristors are the key elements for neuronal networks, as the memory effect represents plasticity of synapses. Neurons have the same ingredients as memristors plus at least one negative resistance that destabilizes the system in a Hopf bifurcation that passes the dynamics from rest to a spiking state. The operation of spiking networks occurs by time domain impulses, but characterization of the material elements is much better done in the frequency domain, by the techniques of impedance spectroscopy. Here we provide the methods to assess memory, plasticity and spiking in the frequency domain, and we show the transformation to the time domain. We present the fundamental model of a halide perovskite memristor, that describes the behaviour both in time and frequency domain. Next, we show the impedance spectroscopy criteria for dynamical regimes of a FitzHugh-Nagumo model, that is a representative minimal model of a spiking neuron. We expand the analysis to cover the possible impedance spectroscopy behaviours of all two-dimensional oscillating systems. In conclusion we show that impedance spectroscopy is a strong characterization method for producing memristors, synapses and neurons with tailored temporal dynamics, hysteresis, and rhythmic oscillations for neuromorphic computing.
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