A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks

Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synapti...
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Autor*in:	Dong‐Ho Kang [verfasserIn] Jeong‐Hoon Kim [verfasserIn] Seyong Oh [verfasserIn] Hyung‐Youl Park [verfasserIn] Sreekantha Reddy Dugasani [verfasserIn] Beom‐Seok Kang [verfasserIn] Changhwan Choi [verfasserIn] Rino Choi [verfasserIn] Sungjoo Lee [verfasserIn] Sung Ha Park [verfasserIn] Keun Heo [verfasserIn] Jin‐Hong Park [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices

Übergeordnetes Werk:	In: Advanced Science - Wiley, 2015, 6(2019), 17, Seite n/a-n/a
Übergeordnetes Werk:	volume:6 ; year:2019 ; number:17 ; pages:n/a-n/a

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1002/advs.201901265

Katalog-ID:	DOAJ039965236

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520			\|a Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.
650		4	\|a handwritten digit pattern recognition
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700	0		\|a Sung Ha Park \|e verfasserin \|4 aut
700	0		\|a Keun Heo \|e verfasserin \|4 aut
700	0		\|a Jin‐Hong Park \|e verfasserin \|4 aut
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allfields	10.1002/advs.201901265 doi (DE-627)DOAJ039965236 (DE-599)DOAJc02151dc904747fba9d3ebf747dcec07 DE-627 ger DE-627 rakwb eng Dong‐Ho Kang verfasserin aut A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy. handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices Science Q Jeong‐Hoon Kim verfasserin aut Seyong Oh verfasserin aut Hyung‐Youl Park verfasserin aut Sreekantha Reddy Dugasani verfasserin aut Beom‐Seok Kang verfasserin aut Changhwan Choi verfasserin aut Rino Choi verfasserin aut Sungjoo Lee verfasserin aut Sung Ha Park verfasserin aut Keun Heo verfasserin aut Jin‐Hong Park verfasserin aut In Advanced Science Wiley, 2015 6(2019), 17, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:6 year:2019 number:17 pages:n/a-n/a https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/article/c02151dc904747fba9d3ebf747dcec07 kostenfrei https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 17 n/a-n/a
spelling	10.1002/advs.201901265 doi (DE-627)DOAJ039965236 (DE-599)DOAJc02151dc904747fba9d3ebf747dcec07 DE-627 ger DE-627 rakwb eng Dong‐Ho Kang verfasserin aut A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy. handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices Science Q Jeong‐Hoon Kim verfasserin aut Seyong Oh verfasserin aut Hyung‐Youl Park verfasserin aut Sreekantha Reddy Dugasani verfasserin aut Beom‐Seok Kang verfasserin aut Changhwan Choi verfasserin aut Rino Choi verfasserin aut Sungjoo Lee verfasserin aut Sung Ha Park verfasserin aut Keun Heo verfasserin aut Jin‐Hong Park verfasserin aut In Advanced Science Wiley, 2015 6(2019), 17, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:6 year:2019 number:17 pages:n/a-n/a https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/article/c02151dc904747fba9d3ebf747dcec07 kostenfrei https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 17 n/a-n/a
allfields_unstemmed	10.1002/advs.201901265 doi (DE-627)DOAJ039965236 (DE-599)DOAJc02151dc904747fba9d3ebf747dcec07 DE-627 ger DE-627 rakwb eng Dong‐Ho Kang verfasserin aut A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy. handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices Science Q Jeong‐Hoon Kim verfasserin aut Seyong Oh verfasserin aut Hyung‐Youl Park verfasserin aut Sreekantha Reddy Dugasani verfasserin aut Beom‐Seok Kang verfasserin aut Changhwan Choi verfasserin aut Rino Choi verfasserin aut Sungjoo Lee verfasserin aut Sung Ha Park verfasserin aut Keun Heo verfasserin aut Jin‐Hong Park verfasserin aut In Advanced Science Wiley, 2015 6(2019), 17, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:6 year:2019 number:17 pages:n/a-n/a https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/article/c02151dc904747fba9d3ebf747dcec07 kostenfrei https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 17 n/a-n/a
allfieldsGer	10.1002/advs.201901265 doi (DE-627)DOAJ039965236 (DE-599)DOAJc02151dc904747fba9d3ebf747dcec07 DE-627 ger DE-627 rakwb eng Dong‐Ho Kang verfasserin aut A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy. handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices Science Q Jeong‐Hoon Kim verfasserin aut Seyong Oh verfasserin aut Hyung‐Youl Park verfasserin aut Sreekantha Reddy Dugasani verfasserin aut Beom‐Seok Kang verfasserin aut Changhwan Choi verfasserin aut Rino Choi verfasserin aut Sungjoo Lee verfasserin aut Sung Ha Park verfasserin aut Keun Heo verfasserin aut Jin‐Hong Park verfasserin aut In Advanced Science Wiley, 2015 6(2019), 17, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:6 year:2019 number:17 pages:n/a-n/a https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/article/c02151dc904747fba9d3ebf747dcec07 kostenfrei https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 17 n/a-n/a
allfieldsSound	10.1002/advs.201901265 doi (DE-627)DOAJ039965236 (DE-599)DOAJc02151dc904747fba9d3ebf747dcec07 DE-627 ger DE-627 rakwb eng Dong‐Ho Kang verfasserin aut A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy. handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices Science Q Jeong‐Hoon Kim verfasserin aut Seyong Oh verfasserin aut Hyung‐Youl Park verfasserin aut Sreekantha Reddy Dugasani verfasserin aut Beom‐Seok Kang verfasserin aut Changhwan Choi verfasserin aut Rino Choi verfasserin aut Sungjoo Lee verfasserin aut Sung Ha Park verfasserin aut Keun Heo verfasserin aut Jin‐Hong Park verfasserin aut In Advanced Science Wiley, 2015 6(2019), 17, Seite n/a-n/a (DE-627)817357777 (DE-600)2808093-2 21983844 nnns volume:6 year:2019 number:17 pages:n/a-n/a https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/article/c02151dc904747fba9d3ebf747dcec07 kostenfrei https://doi.org/10.1002/advs.201901265 kostenfrei https://doaj.org/toc/2198-3844 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 17 n/a-n/a
language	English
source	In Advanced Science 6(2019), 17, Seite n/a-n/a volume:6 year:2019 number:17 pages:n/a-n/a
sourceStr	In Advanced Science 6(2019), 17, Seite n/a-n/a volume:6 year:2019 number:17 pages:n/a-n/a
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices Science Q
isfreeaccess_bool	true
container_title	Advanced Science
authorswithroles_txt_mv	Dong‐Ho Kang @@aut@@ Jeong‐Hoon Kim @@aut@@ Seyong Oh @@aut@@ Hyung‐Youl Park @@aut@@ Sreekantha Reddy Dugasani @@aut@@ Beom‐Seok Kang @@aut@@ Changhwan Choi @@aut@@ Rino Choi @@aut@@ Sungjoo Lee @@aut@@ Sung Ha Park @@aut@@ Keun Heo @@aut@@ Jin‐Hong Park @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	817357777
id	DOAJ039965236
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ039965236</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502081506.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/advs.201901265</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ039965236</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJc02151dc904747fba9d3ebf747dcec07</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Dong‐Ho Kang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). 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author	Dong‐Ho Kang
spellingShingle	Dong‐Ho Kang misc handwritten digit pattern recognition misc neural devices misc neuromorphic devices misc salmon DNA misc synaptic devices misc Science misc Q A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks
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topic_title	A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices
topic	misc handwritten digit pattern recognition misc neural devices misc neuromorphic devices misc salmon DNA misc synaptic devices misc Science misc Q
topic_unstemmed	misc handwritten digit pattern recognition misc neural devices misc neuromorphic devices misc salmon DNA misc synaptic devices misc Science misc Q
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title	A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks
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title_full	A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks
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author_browse	Dong‐Ho Kang Jeong‐Hoon Kim Seyong Oh Hyung‐Youl Park Sreekantha Reddy Dugasani Beom‐Seok Kang Changhwan Choi Rino Choi Sungjoo Lee Sung Ha Park Keun Heo Jin‐Hong Park
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title_sort	neuromorphic device implemented on a salmon‐dna electrolyte and its application to artificial neural networks
title_auth	A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks
abstract	Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.
abstractGer	Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.
abstract_unstemmed	Abstract A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+‐doped salmon deoxyribonucleic acid (S‐DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S‐DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.
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title_short	A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks
url	https://doi.org/10.1002/advs.201901265 https://doaj.org/article/c02151dc904747fba9d3ebf747dcec07 https://doaj.org/toc/2198-3844
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Owing to the slow Cu2+ diffusion through the base pairing sites in the S‐DNA electrolyte, the synaptic operation of the S‐DNA device features special long‐term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S‐DNA‐based neural networks. Furthermore, the representative neuronal operation, “integrate‐and‐fire,” is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S‐DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (\|αp\|: 31→20, \|αd\|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single‐layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). 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A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks

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