{"title":"分流抑制是神经形态结构中神经激发的增殖机制","authors":"Frances S. Chance, S. Cardwell","doi":"10.1145/3584954.3584965","DOIUrl":null,"url":null,"abstract":"Shunting inhibition is a potential mechanism by which biological systems multiply two time-varying signals, most recently proposed in single neurons of the fly visual system. Our work demonstrates this effect in a biological neuron model and the equivalent circuit in neuromorphic hardware modeling dendrites. We present a multi-compartment neuromorphic dendritic model that produces a multiplication-like effect using the shunting inhibition mechanism by varying leakage along the dendritic cable. Dendritic computation in neuromorphic architectures has the potential to increase complexity in single neurons and reduce the energy footprint for neural networks by enabling computation in the interconnect.","PeriodicalId":375527,"journal":{"name":"Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Shunting Inhibition as a Neural-Inspired Mechanism for Multiplication in Neuromorphic Architectures\",\"authors\":\"Frances S. Chance, S. Cardwell\",\"doi\":\"10.1145/3584954.3584965\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Shunting inhibition is a potential mechanism by which biological systems multiply two time-varying signals, most recently proposed in single neurons of the fly visual system. Our work demonstrates this effect in a biological neuron model and the equivalent circuit in neuromorphic hardware modeling dendrites. We present a multi-compartment neuromorphic dendritic model that produces a multiplication-like effect using the shunting inhibition mechanism by varying leakage along the dendritic cable. Dendritic computation in neuromorphic architectures has the potential to increase complexity in single neurons and reduce the energy footprint for neural networks by enabling computation in the interconnect.\",\"PeriodicalId\":375527,\"journal\":{\"name\":\"Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3584954.3584965\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3584954.3584965","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Shunting Inhibition as a Neural-Inspired Mechanism for Multiplication in Neuromorphic Architectures
Shunting inhibition is a potential mechanism by which biological systems multiply two time-varying signals, most recently proposed in single neurons of the fly visual system. Our work demonstrates this effect in a biological neuron model and the equivalent circuit in neuromorphic hardware modeling dendrites. We present a multi-compartment neuromorphic dendritic model that produces a multiplication-like effect using the shunting inhibition mechanism by varying leakage along the dendritic cable. Dendritic computation in neuromorphic architectures has the potential to increase complexity in single neurons and reduce the energy footprint for neural networks by enabling computation in the interconnect.