Artificial synaptic simulating pain-perceptual nociceptor and brain-inspired computing based on Au/Bi3.2La0.8Ti3O12/ITO memristor

Recently, memristors have garnered widespread attention as neuromorphic devices that can simulate synaptic behavior, holding promise for future commercial applications in neuromorphic computing. In this paper, we present a memristor with an Au/Bi3.2La0.8Ti3O12 (BLTO)/ITO structure, demonstrating a switching ratio of nearly 103 over a duration of 104 s. It successfully simulates a range of synaptic behaviors, including long-term potentiation and depression, paired-pulse facilitation, spike-timing-dependent plasticity, spike-rate-dependent plasticity etc. Interestingly, we also employ it to simulate pain threshold, sensitization, and desensitization behaviors of pain-perceptual nociceptor (PPN). Lastly, by introducing memristor differential pairs (1T1R-1T1R), we train a neural network, effectively simplifying the learning process, reducing training time, and achieving a handwriting digit recognition accuracy of up to 97.19 %. Overall, the proposed device holds immense potential in the field of neuromorphic computing, offering possibilities for the next generation of high-performance neuromorphic computing chips.