Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses.

ABSTRACT: Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electron

SUBMITTER: Lin YP 

PROVIDER: S-EPMC5013285 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature