Electronic system with memristive synapses for pattern recognition.

Abstract:

:Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Park S,Chu M,Kim J,Noh J,Jeon M,Hun Lee B,Hwang H,Lee B,Lee BG

doi

10.1038/srep10123

subject

Has Abstract

pub_date

2015-05-05 00:00:00

pages

10123

issn

2045-2322

pii

srep10123

journal_volume

5

pub_type

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