Time and rate dependent synaptic learning in neuro-mimicking resistive memories.

ABSTRACT: Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the functional oxides or engineering the external programming hardware. However, high device-to-device variability in memristors induced by the electroforming process and complicated programming hardware are among the key challenges that hinder achieving biomimetic neuromorphic networks. Here, a simple hybrid complementary metal oxide semiconductor (CMOS)-memristor approach is reported to implement different synaptic learning rules by utilizing a CMOS-compatible memristor based on oxygen-deficient SrTiO_3-x (STO_x). The potential of such

SUBMITTER: Ahmed T 

PROVIDER: S-EPMC6817848 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature