LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm
LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/17/2123 |
| _version_ | 1797521529213288448 |
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| author | Lingfei Mo Minghao Wang |
| author_facet | Lingfei Mo Minghao Wang |
| author_sort | Lingfei Mo |
| collection | DOAJ |
| description | LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities. |
| first_indexed | 2024-03-10T08:13:47Z |
| format | Article |
| id | doaj.art-929188aba0794c049f449daf09537be6 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T08:13:47Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-929188aba0794c049f449daf09537be62023-11-22T10:30:17ZengMDPI AGElectronics2079-92922021-08-011017212310.3390/electronics10172123LogicSNN: A Unified Spiking Neural Networks Logical Operation ParadigmLingfei Mo0Minghao Wang1FutureX LAB, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaFutureX LAB, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaLogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities.https://www.mdpi.com/2079-9292/10/17/2123spiking neural networkslogical operationspike-timing-dependent plasticity |
| spellingShingle | Lingfei Mo Minghao Wang LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm Electronics spiking neural networks logical operation spike-timing-dependent plasticity |
| title | LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm |
| title_full | LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm |
| title_fullStr | LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm |
| title_full_unstemmed | LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm |
| title_short | LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm |
| title_sort | logicsnn a unified spiking neural networks logical operation paradigm |
| topic | spiking neural networks logical operation spike-timing-dependent plasticity |
| url | https://www.mdpi.com/2079-9292/10/17/2123 |
| work_keys_str_mv | AT lingfeimo logicsnnaunifiedspikingneuralnetworkslogicaloperationparadigm AT minghaowang logicsnnaunifiedspikingneuralnetworkslogicaloperationparadigm |