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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.


The recognition results of speech imaginationMeasured output of each integrator during testing mode is shown. When feature code of /a/ is used as an input of memristive HNN, its results is shown in first column. In the cases of /u/ and /i/, its results are also shown in second and third column, respectively.
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f6: The recognition results of speech imaginationMeasured output of each integrator during testing mode is shown. When feature code of /a/ is used as an input of memristive HNN, its results is shown in first column. In the cases of /u/ and /i/, its results are also shown in second and third column, respectively.

Mentions: The output measured at each integrator during the testing mode is shown in fig. 6. The outputs from six integrators for the feature /a/ are shown in the first column. As expected, the integrators’ output drops almost linearly to their saturation value which depends on the conductance of the memristive synapse and the RC time constant of the integrator, at different rates after an initial reset (=4 V). Thus, for /a/, two of the six neurons generate a fire signal as the integrators’ output reaches VTH and the feature code applied to the system is finally recognized as /a/ through the decision logic. After the integration phase, all integrators’ outputs are reset to 4 V during the refractory phase. The output responses for /u/ and /i/ are also shown in fig. 6, which are similar results as for /a/.


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)

The recognition results of speech imaginationMeasured output of each integrator during testing mode is shown. When feature code of /a/ is used as an input of memristive HNN, its results is shown in first column. In the cases of /u/ and /i/, its results are also shown in second and third column, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f6: The recognition results of speech imaginationMeasured output of each integrator during testing mode is shown. When feature code of /a/ is used as an input of memristive HNN, its results is shown in first column. In the cases of /u/ and /i/, its results are also shown in second and third column, respectively.
Mentions: The output measured at each integrator during the testing mode is shown in fig. 6. The outputs from six integrators for the feature /a/ are shown in the first column. As expected, the integrators’ output drops almost linearly to their saturation value which depends on the conductance of the memristive synapse and the RC time constant of the integrator, at different rates after an initial reset (=4 V). Thus, for /a/, two of the six neurons generate a fire signal as the integrators’ output reaches VTH and the feature code applied to the system is finally recognized as /a/ through the decision logic. After the integration phase, all integrators’ outputs are reset to 4 V during the refractory phase. The output responses for /u/ and /i/ are also shown in fig. 6, which are similar results as for /a/.

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.