SYNAPTIC PLASTICITY OF MEMRISTIVE STRUCTURES BASED ON POLY-P-XYLYLENE

Abstract

Abstract—Neuromorphic computer networks (NCNs) with synaptic connections based on memristors can provide much greater efficiency in the hardware implementation of bio-inspired spiking neural networks than digital synaptic elements based on complementary technology. To achieve energy-efficient and, in the long-term, self-learning NCNs, the resistance of a memristor connecting pre- and postsynaptic neurons needs to be changeable according to local rules, e.g., according to the rules of spike-timing-dependent plasticity—STDP. The possibility of memristor training according to STDP rules was demonstrated by the example of Cu/poly-p-xylylene (PPX)/indium tin oxide (ITO) memristive structures, in which the top electrode (copper) acted as the presynaptic input, and the bottom (ITO), as the postsynaptic input. The optimal pulse amplitude and duration values are found for rectangular and triangular training pulses. The results open up prospects for creating autonomous NCNs capable of supervised and unsupervised learning to solve complex cognitive problems.