A modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns
Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spik...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Yahui [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
vertical-cavity surface-emitting laser modified supervised learning rule |
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| Anmerkung: |
© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Science in China - Heidelberg : Springer, 2001, 64(2021), 2 vom: 20. Jan. |
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| Übergeordnetes Werk: |
volume:64 ; year:2021 ; number:2 ; day:20 ; month:01 |
| Links: |
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| DOI / URN: |
10.1007/s11432-020-3040-1 |
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| 520 | |a Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition. | ||
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10.1007/s11432-020-3040-1 doi (DE-627)SPR042831059 (SPR)s11432-020-3040-1-e DE-627 ger DE-627 rakwb eng Zhang, Yahui verfasserin aut A modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition. vertical-cavity surface-emitting laser (dpeaa)DE-He213 modified supervised learning rule (dpeaa)DE-He213 optical spiking neural networks (dpeaa)DE-He213 learning system (dpeaa)DE-He213 pattern recognition (dpeaa)DE-He213 Xiang, Shuiying aut Guo, Xingxing aut Wen, Aijun aut Hao, Yue aut Enthalten in Science in China Heidelberg : Springer, 2001 64(2021), 2 vom: 20. Jan. (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:64 year:2021 number:2 day:20 month:01 https://dx.doi.org/10.1007/s11432-020-3040-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 64 2021 2 20 01 |
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10.1007/s11432-020-3040-1 doi (DE-627)SPR042831059 (SPR)s11432-020-3040-1-e DE-627 ger DE-627 rakwb eng Zhang, Yahui verfasserin aut A modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition. vertical-cavity surface-emitting laser (dpeaa)DE-He213 modified supervised learning rule (dpeaa)DE-He213 optical spiking neural networks (dpeaa)DE-He213 learning system (dpeaa)DE-He213 pattern recognition (dpeaa)DE-He213 Xiang, Shuiying aut Guo, Xingxing aut Wen, Aijun aut Hao, Yue aut Enthalten in Science in China Heidelberg : Springer, 2001 64(2021), 2 vom: 20. Jan. (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:64 year:2021 number:2 day:20 month:01 https://dx.doi.org/10.1007/s11432-020-3040-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 64 2021 2 20 01 |
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10.1007/s11432-020-3040-1 doi (DE-627)SPR042831059 (SPR)s11432-020-3040-1-e DE-627 ger DE-627 rakwb eng Zhang, Yahui verfasserin aut A modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition. vertical-cavity surface-emitting laser (dpeaa)DE-He213 modified supervised learning rule (dpeaa)DE-He213 optical spiking neural networks (dpeaa)DE-He213 learning system (dpeaa)DE-He213 pattern recognition (dpeaa)DE-He213 Xiang, Shuiying aut Guo, Xingxing aut Wen, Aijun aut Hao, Yue aut Enthalten in Science in China Heidelberg : Springer, 2001 64(2021), 2 vom: 20. Jan. (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:64 year:2021 number:2 day:20 month:01 https://dx.doi.org/10.1007/s11432-020-3040-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 64 2021 2 20 01 |
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10.1007/s11432-020-3040-1 doi (DE-627)SPR042831059 (SPR)s11432-020-3040-1-e DE-627 ger DE-627 rakwb eng Zhang, Yahui verfasserin aut A modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition. vertical-cavity surface-emitting laser (dpeaa)DE-He213 modified supervised learning rule (dpeaa)DE-He213 optical spiking neural networks (dpeaa)DE-He213 learning system (dpeaa)DE-He213 pattern recognition (dpeaa)DE-He213 Xiang, Shuiying aut Guo, Xingxing aut Wen, Aijun aut Hao, Yue aut Enthalten in Science in China Heidelberg : Springer, 2001 64(2021), 2 vom: 20. Jan. (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:64 year:2021 number:2 day:20 month:01 https://dx.doi.org/10.1007/s11432-020-3040-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 64 2021 2 20 01 |
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10.1007/s11432-020-3040-1 doi (DE-627)SPR042831059 (SPR)s11432-020-3040-1-e DE-627 ger DE-627 rakwb eng Zhang, Yahui verfasserin aut A modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition. vertical-cavity surface-emitting laser (dpeaa)DE-He213 modified supervised learning rule (dpeaa)DE-He213 optical spiking neural networks (dpeaa)DE-He213 learning system (dpeaa)DE-He213 pattern recognition (dpeaa)DE-He213 Xiang, Shuiying aut Guo, Xingxing aut Wen, Aijun aut Hao, Yue aut Enthalten in Science in China Heidelberg : Springer, 2001 64(2021), 2 vom: 20. Jan. (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:64 year:2021 number:2 day:20 month:01 https://dx.doi.org/10.1007/s11432-020-3040-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 64 2021 2 20 01 |
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modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns |
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A modified supervised learning rule for training a photonic spiking neural network to recognize digital patterns |
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Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract A modified supervised learning rule which is suitable for training photonic spiking neural networks (SNN) is proposed for the first time. The proposed learning rule is independent of the time intervals between actual spike and desired spike or between presynaptic spike and postsynaptic spike. Based on the proposed supervised learning rule, 10 digital images are learned in photonic neural network which consists of 30 presynaptic neurons and 10 postsynaptic neurons. Presynaptic and postsynaptic neurons are photonic neurons based on vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSEL-SA). The results show that 10 digital images are recognized correctly in photonic SNN after enough training. Additionally, the effects of learning rate, the jitters of learning rate, initial weights distribution of SNN and bias current of postsynaptic neurons (VCSELs-SA) on the recognized error are examined carefully based on the proposed learning rule. To the best of our knowledge, such modified supervised learning rule has not yet been reported, which would contribute to training photonic neural networks, and hence is interesting for neuromorphic photonic systems and pattern recognition. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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