Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks.

ABSTRACT: Shallow feed-forward networks are incapable of addressing complex tasks such as natural language processing that require learning of temporal signals. To address these requirements, we need deep neuromorphic architectures with recurrent connections such as deep recurrent neural networks. However, the training of such networks demand very high precision of weights, excellent conductance linearity and low write-noise- not satisfied by current memristive implementations. Inspired from optogenetics, here we report a neuromorphic computing platform comprised of photo-excitable neuristors capable of in-memory computations across 980 addressable states with a high signal-to-noise ratio of 77. The large linear dynamic range, low write noise and selective excitability allows high fidelity opto-elec

SUBMITTER: John RA 

PROVIDER: S-EPMC7316775 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature