Eashwar M.V. , Nivetha T. , Bindu B. , Noor Ain Kamsani
{"title":"基于运算放大器的LIF神经元和RRAM突触阵列的脉冲神经网络电路","authors":"Eashwar M.V. , Nivetha T. , Bindu B. , Noor Ain Kamsani","doi":"10.1016/j.aeue.2025.156004","DOIUrl":null,"url":null,"abstract":"<div><div>Spiking Neural Networks (SNNs) have become crucial in neuromorphic computing for efficiently processing the vast amounts of digital data generated in the technology-driven world. The resistive RAM (RRAM) based SNNs offer superior energy efficiency, high-speed processing, parallelism, and scalability for neuromorphic computing applications. In this article, an SNN circuit with a 1T-1R RRAM synaptic array along with op-amp and 555 timer-based leaky integrate-and-fire (LIF) neuron is implemented to use for pattern recognition. The input pre-spikes from the pattern are applied to the RRAM synaptic array, which exhibits synaptic plasticity. The LIF neuron processes the synaptic array output to produce post-spikes, which modify the conductance of the RRAM synaptic array based on the spike-timing-dependent plasticity (STDP) mechanism. The unique output spikes obtained for different characters can be used for pattern recognition of the characters.</div></div>","PeriodicalId":50844,"journal":{"name":"Aeu-International Journal of Electronics and Communications","volume":"201 ","pages":"Article 156004"},"PeriodicalIF":3.2000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spiking neural network circuit with op-amp based LIF neuron and RRAM synaptic array\",\"authors\":\"Eashwar M.V. , Nivetha T. , Bindu B. , Noor Ain Kamsani\",\"doi\":\"10.1016/j.aeue.2025.156004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Spiking Neural Networks (SNNs) have become crucial in neuromorphic computing for efficiently processing the vast amounts of digital data generated in the technology-driven world. The resistive RAM (RRAM) based SNNs offer superior energy efficiency, high-speed processing, parallelism, and scalability for neuromorphic computing applications. In this article, an SNN circuit with a 1T-1R RRAM synaptic array along with op-amp and 555 timer-based leaky integrate-and-fire (LIF) neuron is implemented to use for pattern recognition. The input pre-spikes from the pattern are applied to the RRAM synaptic array, which exhibits synaptic plasticity. The LIF neuron processes the synaptic array output to produce post-spikes, which modify the conductance of the RRAM synaptic array based on the spike-timing-dependent plasticity (STDP) mechanism. The unique output spikes obtained for different characters can be used for pattern recognition of the characters.</div></div>\",\"PeriodicalId\":50844,\"journal\":{\"name\":\"Aeu-International Journal of Electronics and Communications\",\"volume\":\"201 \",\"pages\":\"Article 156004\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Aeu-International Journal of Electronics and Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1434841125003450\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aeu-International Journal of Electronics and Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1434841125003450","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Spiking neural network circuit with op-amp based LIF neuron and RRAM synaptic array
Spiking Neural Networks (SNNs) have become crucial in neuromorphic computing for efficiently processing the vast amounts of digital data generated in the technology-driven world. The resistive RAM (RRAM) based SNNs offer superior energy efficiency, high-speed processing, parallelism, and scalability for neuromorphic computing applications. In this article, an SNN circuit with a 1T-1R RRAM synaptic array along with op-amp and 555 timer-based leaky integrate-and-fire (LIF) neuron is implemented to use for pattern recognition. The input pre-spikes from the pattern are applied to the RRAM synaptic array, which exhibits synaptic plasticity. The LIF neuron processes the synaptic array output to produce post-spikes, which modify the conductance of the RRAM synaptic array based on the spike-timing-dependent plasticity (STDP) mechanism. The unique output spikes obtained for different characters can be used for pattern recognition of the characters.
期刊介绍:
AEÜ is an international scientific journal which publishes both original works and invited tutorials. The journal''s scope covers all aspects of theory and design of circuits, systems and devices for electronics, signal processing, and communication, including:
signal and system theory, digital signal processing
network theory and circuit design
information theory, communication theory and techniques, modulation, source and channel coding
switching theory and techniques, communication protocols
optical communications
microwave theory and techniques, radar, sonar
antennas, wave propagation
AEÜ publishes full papers and letters with very short turn around time but a high standard review process. Review cycles are typically finished within twelve weeks by application of modern electronic communication facilities.