Syed Ahmed Aamir, Paul Müller, Laura Kriener, G. Kiene, J. Schemmel, K. Meier
{"title":"从LIF到AdEx神经元模型:加速模拟65纳米CMOS实现","authors":"Syed Ahmed Aamir, Paul Müller, Laura Kriener, G. Kiene, J. Schemmel, K. Meier","doi":"10.1109/BIOCAS.2017.8325167","DOIUrl":null,"url":null,"abstract":"Here we present analog circuits emulating an Adaptive Exponential I&F (AdEx) neuron model developed for our second generation 65-nm CMOS neuromorphic hardware. Designed for an existing accelerated Leaky Integrate and Fire (LIF) circuit, the modular circuit architecture allows us to switch between LIF and AdEx neuron models and further to multiple-compartments. We describe our circuit implementation and the simulation results for adaptation and exponential sub-circuits. The neuron circuit specifications are compared with a targeted set of computational models. We show how addition of analog AdEx circuits let us qualitatively reproduce spike patterns known from cortical neurons.","PeriodicalId":361477,"journal":{"name":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"From LIF to AdEx neuron models: Accelerated analog 65 nm CMOS implementation\",\"authors\":\"Syed Ahmed Aamir, Paul Müller, Laura Kriener, G. Kiene, J. Schemmel, K. Meier\",\"doi\":\"10.1109/BIOCAS.2017.8325167\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Here we present analog circuits emulating an Adaptive Exponential I&F (AdEx) neuron model developed for our second generation 65-nm CMOS neuromorphic hardware. Designed for an existing accelerated Leaky Integrate and Fire (LIF) circuit, the modular circuit architecture allows us to switch between LIF and AdEx neuron models and further to multiple-compartments. We describe our circuit implementation and the simulation results for adaptation and exponential sub-circuits. The neuron circuit specifications are compared with a targeted set of computational models. We show how addition of analog AdEx circuits let us qualitatively reproduce spike patterns known from cortical neurons.\",\"PeriodicalId\":361477,\"journal\":{\"name\":\"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)\",\"volume\":\"112 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIOCAS.2017.8325167\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIOCAS.2017.8325167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
摘要
在这里,我们提出了模拟电路,模拟了为我们的第二代65纳米CMOS神经形态硬件开发的自适应指数I&F (AdEx)神经元模型。专为现有的加速Leaky integrated and Fire (LIF)电路设计,模块化电路架构允许我们在LIF和AdEx神经元模型之间切换,并进一步切换为多室。我们描述了我们的电路实现和自适应和指数子电路的仿真结果。神经元电路规格与一组目标计算模型进行比较。我们展示了如何添加模拟AdEx电路,让我们定性地再现从皮质神经元已知的尖峰模式。
From LIF to AdEx neuron models: Accelerated analog 65 nm CMOS implementation
Here we present analog circuits emulating an Adaptive Exponential I&F (AdEx) neuron model developed for our second generation 65-nm CMOS neuromorphic hardware. Designed for an existing accelerated Leaky Integrate and Fire (LIF) circuit, the modular circuit architecture allows us to switch between LIF and AdEx neuron models and further to multiple-compartments. We describe our circuit implementation and the simulation results for adaptation and exponential sub-circuits. The neuron circuit specifications are compared with a targeted set of computational models. We show how addition of analog AdEx circuits let us qualitatively reproduce spike patterns known from cortical neurons.