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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-09-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.201901265 |
| _version_ | 1818172447717851136 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Dong‐Ho Kang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T19:12:46Z |
| format | Article |
| id | doaj.art-c02151dc904747fba9d3ebf747dcec07 |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-12-11T19:12:46Z |
| publishDate | 2019-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-c02151dc904747fba9d3ebf747dcec072022-12-22T00:53:43ZengWileyAdvanced Science2198-38442019-09-01617n/an/a10.1002/advs.201901265A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural NetworksDong‐Ho Kang0Jeong‐Hoon Kim1Seyong Oh2Hyung‐Youl Park3Sreekantha Reddy Dugasani4Beom‐Seok Kang5Changhwan Choi6Rino Choi7Sungjoo Lee8Sung Ha Park9Keun Heo10Jin‐Hong Park11Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 KoreaDepartment of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 KoreaDepartment of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 KoreaDepartment of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 KoreaDepartment of Physics Sungkyunkwan University Suwon 440‐746 South KoreaDepartment of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 KoreaDivision of Materials Science and Engineering Hanyang University Seoul 133–791 South KoreaMaterial Science and Engineering Inha University Incheon 402–751 South KoreaSKKU Advanced Institute of Nanotechnology (SAINT) Sungkyunkwan University Suwon 440–746 South KoreaDepartment of Physics Sungkyunkwan University Suwon 440‐746 South KoreaDepartment of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 KoreaDepartment of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 KoreaAbstract 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.https://doi.org/10.1002/advs.201901265handwritten digit pattern recognitionneural devicesneuromorphic devicessalmon DNAsynaptic devices |
| spellingShingle | 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 A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks Advanced Science handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices |
| title | A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks |
| title_full | A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks |
| title_fullStr | A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks |
| title_full_unstemmed | A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks |
| title_short | A Neuromorphic Device Implemented on a Salmon‐DNA Electrolyte and its Application to Artificial Neural Networks |
| title_sort | neuromorphic device implemented on a salmon dna electrolyte and its application to artificial neural networks |
| topic | handwritten digit pattern recognition neural devices neuromorphic devices salmon DNA synaptic devices |
| url | https://doi.org/10.1002/advs.201901265 |
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