Supervised learning in spiking neural networks with synaptic delay-weight plasticity
Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learn...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Malu [verfasserIn] Wu, Jibin [verfasserIn] Belatreche, Ammar [verfasserIn] Pan, Zihan [verfasserIn] Xie, Xiurui [verfasserIn] Chua, Yansong [verfasserIn] Li, Guoqi [verfasserIn] Qu, Hong [verfasserIn] Li, Haizhou [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Neurocomputing - Amsterdam : Elsevier, 1989, 409, Seite 103-118 |
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| Übergeordnetes Werk: |
volume:409 ; pages:103-118 |
| DOI / URN: |
10.1016/j.neucom.2020.03.079 |
|---|
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ELV004501187 |
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| 245 | 1 | 0 | |a Supervised learning in spiking neural networks with synaptic delay-weight plasticity |
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| 520 | |a Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways. | ||
| 650 | 4 | |a Spiking neurons | |
| 650 | 4 | |a Spiking neural networks | |
| 650 | 4 | |a Supervised learning | |
| 650 | 4 | |a Synaptic plasticity | |
| 650 | 4 | |a Synaptic weight | |
| 650 | 4 | |a Synaptic delay | |
| 700 | 1 | |a Wu, Jibin |e verfasserin |4 aut | |
| 700 | 1 | |a Belatreche, Ammar |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Zihan |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Xiurui |e verfasserin |4 aut | |
| 700 | 1 | |a Chua, Yansong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Guoqi |e verfasserin |4 aut | |
| 700 | 1 | |a Qu, Hong |e verfasserin |0 (orcid)0000-0001-6114-3441 |4 aut | |
| 700 | 1 | |a Li, Haizhou |e verfasserin |4 aut | |
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10.1016/j.neucom.2020.03.079 doi (DE-627)ELV004501187 (ELSEVIER)S0925-2312(20)30466-5 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Malu verfasserin aut Supervised learning in spiking neural networks with synaptic delay-weight plasticity 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways. Spiking neurons Spiking neural networks Supervised learning Synaptic plasticity Synaptic weight Synaptic delay Wu, Jibin verfasserin aut Belatreche, Ammar verfasserin aut Pan, Zihan verfasserin aut Xie, Xiurui verfasserin aut Chua, Yansong verfasserin aut Li, Guoqi verfasserin aut Qu, Hong verfasserin (orcid)0000-0001-6114-3441 aut Li, Haizhou verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 409, Seite 103-118 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:409 pages:103-118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 409 103-118 |
| spelling |
10.1016/j.neucom.2020.03.079 doi (DE-627)ELV004501187 (ELSEVIER)S0925-2312(20)30466-5 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Malu verfasserin aut Supervised learning in spiking neural networks with synaptic delay-weight plasticity 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways. Spiking neurons Spiking neural networks Supervised learning Synaptic plasticity Synaptic weight Synaptic delay Wu, Jibin verfasserin aut Belatreche, Ammar verfasserin aut Pan, Zihan verfasserin aut Xie, Xiurui verfasserin aut Chua, Yansong verfasserin aut Li, Guoqi verfasserin aut Qu, Hong verfasserin (orcid)0000-0001-6114-3441 aut Li, Haizhou verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 409, Seite 103-118 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:409 pages:103-118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 409 103-118 |
| allfields_unstemmed |
10.1016/j.neucom.2020.03.079 doi (DE-627)ELV004501187 (ELSEVIER)S0925-2312(20)30466-5 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Malu verfasserin aut Supervised learning in spiking neural networks with synaptic delay-weight plasticity 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways. Spiking neurons Spiking neural networks Supervised learning Synaptic plasticity Synaptic weight Synaptic delay Wu, Jibin verfasserin aut Belatreche, Ammar verfasserin aut Pan, Zihan verfasserin aut Xie, Xiurui verfasserin aut Chua, Yansong verfasserin aut Li, Guoqi verfasserin aut Qu, Hong verfasserin (orcid)0000-0001-6114-3441 aut Li, Haizhou verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 409, Seite 103-118 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:409 pages:103-118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 409 103-118 |
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10.1016/j.neucom.2020.03.079 doi (DE-627)ELV004501187 (ELSEVIER)S0925-2312(20)30466-5 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Malu verfasserin aut Supervised learning in spiking neural networks with synaptic delay-weight plasticity 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways. Spiking neurons Spiking neural networks Supervised learning Synaptic plasticity Synaptic weight Synaptic delay Wu, Jibin verfasserin aut Belatreche, Ammar verfasserin aut Pan, Zihan verfasserin aut Xie, Xiurui verfasserin aut Chua, Yansong verfasserin aut Li, Guoqi verfasserin aut Qu, Hong verfasserin (orcid)0000-0001-6114-3441 aut Li, Haizhou verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 409, Seite 103-118 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:409 pages:103-118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 409 103-118 |
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10.1016/j.neucom.2020.03.079 doi (DE-627)ELV004501187 (ELSEVIER)S0925-2312(20)30466-5 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Zhang, Malu verfasserin aut Supervised learning in spiking neural networks with synaptic delay-weight plasticity 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways. Spiking neurons Spiking neural networks Supervised learning Synaptic plasticity Synaptic weight Synaptic delay Wu, Jibin verfasserin aut Belatreche, Ammar verfasserin aut Pan, Zihan verfasserin aut Xie, Xiurui verfasserin aut Chua, Yansong verfasserin aut Li, Guoqi verfasserin aut Qu, Hong verfasserin (orcid)0000-0001-6114-3441 aut Li, Haizhou verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 409, Seite 103-118 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:409 pages:103-118 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 409 103-118 |
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Zhang, Malu ddc 610 bkl 54.72 misc Spiking neurons misc Spiking neural networks misc Supervised learning misc Synaptic plasticity misc Synaptic weight misc Synaptic delay Supervised learning in spiking neural networks with synaptic delay-weight plasticity |
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610 DE-600 54.72 bkl Supervised learning in spiking neural networks with synaptic delay-weight plasticity Spiking neurons Spiking neural networks Supervised learning Synaptic plasticity Synaptic weight Synaptic delay |
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Supervised learning in spiking neural networks with synaptic delay-weight plasticity |
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Zhang, Malu Wu, Jibin Belatreche, Ammar Pan, Zihan Xie, Xiurui Chua, Yansong Li, Guoqi Qu, Hong Li, Haizhou |
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supervised learning in spiking neural networks with synaptic delay-weight plasticity |
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Supervised learning in spiking neural networks with synaptic delay-weight plasticity |
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Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways. |
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Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways. |
| abstract_unstemmed |
Spiking neurons encode information through their spiking temporal patterns. Although the precise spike-timing based encoding scheme has long been recognised, the exact mechanism that underlies the learning of such precise spike-timing in the brain remains an open question. Most of the existing learning methods for spiking neurons are based on synaptic weight adjustment. However, biological evidences suggest that synaptic delays can also be modulated to play an important role in the learning process. This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity. Remote Supervised Method (ReSuMe) and Perceptron Based Spiking Neuron Learning Rule (PBSNLR), two representative supervised learning methods, are studied to illustrate how the synaptic delay-weight plasticity works. The performance of the proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. The experiments show that the synaptic delay-weight learning method outperforms the traditional synaptic weight learning methods in many ways. |
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Wu, Jibin Belatreche, Ammar Pan, Zihan Xie, Xiurui Chua, Yansong Li, Guoqi Qu, Hong Li, Haizhou |
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