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Electronic system with memristive synapses for pattern recognition.

Park S, Chu M, Kim J, Noh J, Jeon M, Hun Lee B, Hwang H, Lee B, Lee BG - Sci Rep (2015)

Bottom Line: 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.Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.

View Article: PubMed Central - PubMed

Affiliation: Department of Nanobio Materials and Electronics, Gwangju Institute of Science and Technology, Gwangju, Korea 500-712.

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.

No MeSH data available.


EEG analysis and processing(a) Experimental paradigm for EEG study consists of four parts. (b) EEG data analysis. (c) Signal processing.
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f2: EEG analysis and processing(a) Experimental paradigm for EEG study consists of four parts. (b) EEG data analysis. (c) Signal processing.

Mentions: The experimental paradigm for this study is described in fig. 2(a). The acquired EEG data are segmented according to the trial and the stimuli associated with /a/, /i/, and /u/ (Fig. 2(b), top left). The segmented data are then analysed to identify the distinct features of the three experimental conditions (/a/, /i/, and /u/).


Electronic system with memristive synapses for pattern recognition.

Park S, Chu M, Kim J, Noh J, Jeon M, Hun Lee B, Hwang H, Lee B, Lee BG - Sci Rep (2015)

EEG analysis and processing(a) Experimental paradigm for EEG study consists of four parts. (b) EEG data analysis. (c) Signal processing.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4419523&req=5

f2: EEG analysis and processing(a) Experimental paradigm for EEG study consists of four parts. (b) EEG data analysis. (c) Signal processing.
Mentions: The experimental paradigm for this study is described in fig. 2(a). The acquired EEG data are segmented according to the trial and the stimuli associated with /a/, /i/, and /u/ (Fig. 2(b), top left). The segmented data are then analysed to identify the distinct features of the three experimental conditions (/a/, /i/, and /u/).

Bottom Line: 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.Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.

View Article: PubMed Central - PubMed

Affiliation: Department of Nanobio Materials and Electronics, Gwangju Institute of Science and Technology, Gwangju, Korea 500-712.

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.

No MeSH data available.