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Article Abstract
In this study we propose a new class of artificial neurons and memristors made of active chiral particles. We formulate a single-particle model to simulate active chiral particle behavior in a two-terminal device, with resistance depending on the particle position. We create a dynamical phase map connecting particle trajectories and memristor electrical properties to applied voltage and particle's self-propulsion parameters. Analysis of spiking modes in artificial neurons, with and without noise, shows the memristor switches between high- and low-resistance states, exhibiting stable limit cycles in the position-voltage phase response.
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