{"doi":"10.1109/newcas.2018.8585554","title":"An Analog Implementation of FitzHugh-Nagumo Neuron Model for Spiking Neural Networks","abstract":null,"journal":"2018 16th IEEE International New Circuits and Systems Conference (NEWCAS)","year":2018,"id":659399,"datarank":0.31191623125197543,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.0,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1721311,"name":"Anurag Desai","orcid":null,"position":1,"is_corresponding":false},{"id":1721312,"name":"Mohammad R. Haider","orcid":null,"position":2,"is_corresponding":false},{"id":1721313,"name":"Reinhold Ludwig","orcid":null,"position":3,"is_corresponding":false},{"id":1721314,"name":"Yehia Massoud","orcid":null,"position":4,"is_corresponding":false},{"id":1721310,"name":"Raunak Borwankar","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"An Analog Implementation of FitzHugh-Nagumo Neuron Model for Spiking Neural Networks","abstract":"A low power analog implementation of FitzHugh-Nagumo (FHN) neuron model is presented in this paper for large scale spiking neural network and neuromorphic algorithm realization. The FHN neuron model is designed using log-domain low pass filters and translinear multipliers to emulate voltage-like variable with cubic non-linearity and a recovery variable. Various spiking behaviors observed in biological neurons are demonstrated in simulation results. The neuron model was designed in 45 nm CMOS process which has 1.6 nW and 40 nW power consumption at rest and for a single spiking event respectively.","is_dataset_classified":null,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W2906832317","authors":[],"funders":[],"total_grants":0,"fwci":0.3896,"citation_percentile":0.55140238,"influential_citations":0,"citation_trend":[{"year":2021,"count":1},{"year":2022,"count":3},{"year":2023,"count":2},{"year":2024,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/8572699/8585422/08585554.pdf?arnumber=8585554","host_type":"publisher"},{"url":"https://doi.org/10.1109/newcas.2018.8585554","host_type":""}],"fields_of_study":["Advanced Memory and Neural Computing","Neural dynamics and brain function","Neural Networks and Applications"],"mesh_terms":[],"keywords":["Neuromorphic engineering","Spiking neural network","Biological neuron model","Computer science","Realization (probability)","Artificial neural network","CMOS","Power consumption","Neuron","Variable (mathematics)","Spike (software development)","Electronic engineering","Power (physics)","Artificial intelligence","Mathematics","Neuroscience","Physics","Engineering"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Affordable and clean energy"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T06:49:13.266671Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}