Learning to Approximate Functions Using Nb-Doped SrTiO₃ Memristors.

ABSTRACT: Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilized Nb-doped SrTiO₃ memristors as synapses in a simulated neural network by arranging them into differential synaptic pairs, with the weight of the connection given by the difference in normalized conductance values between the two paired memristors. This network learned to represent functions through a training process based on a novel supervised learning algorithm, during which discrete voltage pulses were applied to one of the two memristors in each pair. To simulate the fact

SUBMITTER: Tiotto TF 

PROVIDER: S-EPMC7933504 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature