All-optical spiking neural network and optical spike-time-dependent plasticity based on the self-pulsing effect within a micro-ring resonator
In this paper, we proposed an all-optical version of photonic spiking neurons and spike-time-dependent plasticity (STDP) based on the nonlinear optical effects within a micro-ring resonator. In this system, the self-pulsing effect was exploited to implement threshold control, and the equivalent puls...
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Veröffentlicht in: | Applied optics (2004) 2023-07, Vol.62 (20), p.5459-5466 |
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container_title | Applied optics (2004) |
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creator | Wen, Jin Zhang, Hui Wu, Zhengwei Wang, Qian Yu, Huimin Sun, Wei Liang, Bozhi He, Chenyao Xiong, Keyu Pan, Yu Zhang, Ying Liu, Zhanzhi |
description | In this paper, we proposed an all-optical version of photonic spiking neurons and spike-time-dependent plasticity (STDP) based on the nonlinear optical effects within a micro-ring resonator. In this system, the self-pulsing effect was exploited to implement threshold control, and the equivalent pulse energy required for spiking, calculated by multiplying the input pulse power amplitude with its duration, was about 14.1 pJ. The positive performance of the neurons in the excitability and cascadability tests validated the feasibility of this scheme. Furthermore, two simulations were performed to demonstrate that such an all-optical spiking neural network incorporated with STDP could run stably on a stochastic topology. The essence of such an all-optical spiking neural network is a nonlinear spiking dynamical system that combines the advantages of photonics and spiking neural networks (SNNs), promising access to the high speed and lower consumption inherent to optical systems. |
doi_str_mv | 10.1364/AO.493466 |
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In this system, the self-pulsing effect was exploited to implement threshold control, and the equivalent pulse energy required for spiking, calculated by multiplying the input pulse power amplitude with its duration, was about 14.1 pJ. The positive performance of the neurons in the excitability and cascadability tests validated the feasibility of this scheme. Furthermore, two simulations were performed to demonstrate that such an all-optical spiking neural network incorporated with STDP could run stably on a stochastic topology. The essence of such an all-optical spiking neural network is a nonlinear spiking dynamical system that combines the advantages of photonics and spiking neural networks (SNNs), promising access to the high speed and lower consumption inherent to optical systems.</description><identifier>ISSN: 1559-128X</identifier><identifier>EISSN: 2155-3165</identifier><identifier>EISSN: 1539-4522</identifier><identifier>DOI: 10.1364/AO.493466</identifier><language>eng</language><publisher>Washington: Optical Society of America</publisher><subject>Dynamical systems ; Neural networks ; Neurons ; Nonlinear optics ; Photonics ; Plastic properties ; Resonators ; Spiking ; Time dependence ; Topology</subject><ispartof>Applied optics (2004), 2023-07, Vol.62 (20), p.5459-5466</ispartof><rights>Copyright Optical Society of America Jul 10, 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c290t-33034defc497db4684ea7c9f93c1226b80dcce168d70f8e2347f32df5de5494a3</citedby><cites>FETCH-LOGICAL-c290t-33034defc497db4684ea7c9f93c1226b80dcce168d70f8e2347f32df5de5494a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,3245,27905,27906</link.rule.ids></links><search><creatorcontrib>Wen, Jin</creatorcontrib><creatorcontrib>Zhang, Hui</creatorcontrib><creatorcontrib>Wu, Zhengwei</creatorcontrib><creatorcontrib>Wang, Qian</creatorcontrib><creatorcontrib>Yu, Huimin</creatorcontrib><creatorcontrib>Sun, Wei</creatorcontrib><creatorcontrib>Liang, Bozhi</creatorcontrib><creatorcontrib>He, Chenyao</creatorcontrib><creatorcontrib>Xiong, Keyu</creatorcontrib><creatorcontrib>Pan, Yu</creatorcontrib><creatorcontrib>Zhang, Ying</creatorcontrib><creatorcontrib>Liu, Zhanzhi</creatorcontrib><title>All-optical spiking neural network and optical spike-time-dependent plasticity based on the self-pulsing effect within a micro-ring resonator</title><title>Applied optics (2004)</title><description>In this paper, we proposed an all-optical version of photonic spiking neurons and spike-time-dependent plasticity (STDP) based on the nonlinear optical effects within a micro-ring resonator. In this system, the self-pulsing effect was exploited to implement threshold control, and the equivalent pulse energy required for spiking, calculated by multiplying the input pulse power amplitude with its duration, was about 14.1 pJ. The positive performance of the neurons in the excitability and cascadability tests validated the feasibility of this scheme. Furthermore, two simulations were performed to demonstrate that such an all-optical spiking neural network incorporated with STDP could run stably on a stochastic topology. The essence of such an all-optical spiking neural network is a nonlinear spiking dynamical system that combines the advantages of photonics and spiking neural networks (SNNs), promising access to the high speed and lower consumption inherent to optical systems.</description><subject>Dynamical systems</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Nonlinear optics</subject><subject>Photonics</subject><subject>Plastic properties</subject><subject>Resonators</subject><subject>Spiking</subject><subject>Time dependence</subject><subject>Topology</subject><issn>1559-128X</issn><issn>2155-3165</issn><issn>1539-4522</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpdkc1qGzEUhUVpoK6TRd5A0E27UKq_kUdLE5omEPCmhewGWbqq5ciaqaQh-CHyzpVxFqGrey_n43APB6FrRm-YUPL7enMjtZBKfUALzrqOCKa6j2jRVk0Y758-oc-l7CkVndSrBXpdx0jGqQZrIi5TeA7pD04w53YmqC9jfsYmOfweAVLDAYiDCZKDVPEUTWlyqEe8NQUanXDdAS4QPZnmWE6m4D3Yil9C3YWEDT4Em0eST1KGMiZTx3yJLryJBa7e5hL9vvvx6_aePG5-PtyuH4nlmlYiBBXSgbctgttK1UswK6u9FpZxrrY9ddYCU71bUd8DF3LlBXe-c9BSSyOW6OvZd8rj3xlKHQ6hWIjRJBjnMvBeSU1pr2VDv_yH7sc5p_Zdo4SirNecN-rbmWqZSsnghymHg8nHgdHhVMyw3gznYsQ_FZeCog</recordid><startdate>20230710</startdate><enddate>20230710</enddate><creator>Wen, Jin</creator><creator>Zhang, Hui</creator><creator>Wu, Zhengwei</creator><creator>Wang, Qian</creator><creator>Yu, Huimin</creator><creator>Sun, Wei</creator><creator>Liang, Bozhi</creator><creator>He, Chenyao</creator><creator>Xiong, Keyu</creator><creator>Pan, Yu</creator><creator>Zhang, Ying</creator><creator>Liu, Zhanzhi</creator><general>Optical Society of America</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>7X8</scope></search><sort><creationdate>20230710</creationdate><title>All-optical spiking neural network and optical spike-time-dependent plasticity based on the self-pulsing effect within a micro-ring resonator</title><author>Wen, Jin ; Zhang, Hui ; Wu, Zhengwei ; Wang, Qian ; Yu, Huimin ; Sun, Wei ; Liang, Bozhi ; He, Chenyao ; Xiong, Keyu ; Pan, Yu ; Zhang, Ying ; Liu, Zhanzhi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c290t-33034defc497db4684ea7c9f93c1226b80dcce168d70f8e2347f32df5de5494a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Dynamical systems</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Nonlinear optics</topic><topic>Photonics</topic><topic>Plastic properties</topic><topic>Resonators</topic><topic>Spiking</topic><topic>Time dependence</topic><topic>Topology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wen, Jin</creatorcontrib><creatorcontrib>Zhang, Hui</creatorcontrib><creatorcontrib>Wu, Zhengwei</creatorcontrib><creatorcontrib>Wang, Qian</creatorcontrib><creatorcontrib>Yu, Huimin</creatorcontrib><creatorcontrib>Sun, Wei</creatorcontrib><creatorcontrib>Liang, Bozhi</creatorcontrib><creatorcontrib>He, Chenyao</creatorcontrib><creatorcontrib>Xiong, Keyu</creatorcontrib><creatorcontrib>Pan, Yu</creatorcontrib><creatorcontrib>Zhang, Ying</creatorcontrib><creatorcontrib>Liu, Zhanzhi</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Applied optics (2004)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wen, Jin</au><au>Zhang, Hui</au><au>Wu, Zhengwei</au><au>Wang, Qian</au><au>Yu, Huimin</au><au>Sun, Wei</au><au>Liang, Bozhi</au><au>He, Chenyao</au><au>Xiong, Keyu</au><au>Pan, Yu</au><au>Zhang, Ying</au><au>Liu, Zhanzhi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>All-optical spiking neural network and optical spike-time-dependent plasticity based on the self-pulsing effect within a micro-ring resonator</atitle><jtitle>Applied optics (2004)</jtitle><date>2023-07-10</date><risdate>2023</risdate><volume>62</volume><issue>20</issue><spage>5459</spage><epage>5466</epage><pages>5459-5466</pages><issn>1559-128X</issn><eissn>2155-3165</eissn><eissn>1539-4522</eissn><abstract>In this paper, we proposed an all-optical version of photonic spiking neurons and spike-time-dependent plasticity (STDP) based on the nonlinear optical effects within a micro-ring resonator. In this system, the self-pulsing effect was exploited to implement threshold control, and the equivalent pulse energy required for spiking, calculated by multiplying the input pulse power amplitude with its duration, was about 14.1 pJ. The positive performance of the neurons in the excitability and cascadability tests validated the feasibility of this scheme. Furthermore, two simulations were performed to demonstrate that such an all-optical spiking neural network incorporated with STDP could run stably on a stochastic topology. The essence of such an all-optical spiking neural network is a nonlinear spiking dynamical system that combines the advantages of photonics and spiking neural networks (SNNs), promising access to the high speed and lower consumption inherent to optical systems.</abstract><cop>Washington</cop><pub>Optical Society of America</pub><doi>10.1364/AO.493466</doi><tpages>8</tpages></addata></record> |
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source | Alma/SFX Local Collection; Optica Publishing Group Journals |
subjects | Dynamical systems Neural networks Neurons Nonlinear optics Photonics Plastic properties Resonators Spiking Time dependence Topology |
title | All-optical spiking neural network and optical spike-time-dependent plasticity based on the self-pulsing effect within a micro-ring resonator |
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