2024/09/14 by Agustín Bou, Bou, Agustin, Patricio Ramı́rez +3
Engineering · Neuroscience · #Advanced Memory and Neural Computing #Applied Physics (physics.app-ph) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Ferroelectric and Negative Capacitance Devices #Neuroscience and Neural Engineering
paper · pdf · doi:10.48550/arxiv.2409.09327
openalex publication_date 2024/09/14 · openalex created_date 2024/10/24 · openalex updated_date 2026/07/28
Memristors have been positioned at the forefront of the purposes for carrying out neuromorphic computation. Their tuneable conductivity properties enable the imitation of synaptic behaviour. Multipore nanofluidic memristors have shown their memristic properties and are candidate devices for liquid neuromorphic systems. Such properties are visible through an inductive hysteresis in the current-voltage sweeps, which is then confirmed by the inductive characteristics in impedance spectroscopy measurements. The dynamic behaviour of memristors is largely determined by a voltage-dependent relaxation time. Here, we obtain the kinetic relaxation time of a multipore nanofluidic memristor via its impedance spectra. We show that the behaviour of this characteristic of memristors is comparable to that of natural neural systems. Hence, we open a way to study the mimic of neuron characteristics by searching for memristors with the same kinetic times.