{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T08:10:07Z","timestamp":1725264607815},"reference-count":32,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Circuit Theory & Apps"],"published-print":{"date-parts":[[2021,8]]},"abstract":"Summary<\/jats:title>Hardware implementations of spiking neural networks, which are known as neuromorphic architectures, provide an explicit understanding of brain performance. As a result, biological features of the brain may well inspire the next generation of computers and electronic systems used in such areas as signal processing, image processing, function approximation, and pattern recognition. Approximating nonlinear functions has many uses in computer science and applied mathematics. The sigmoid is the most universal activation function in neural networks by which the relationship between biological and artificial neurons is defined. It is a suitable option for predicting the probability of anything from 0 to 1 as output. In this paper, a spiking neural network using Izhikevich neurons and a gradient descent learning algorithm are propounded to approximate the sigmoid and other nonlinear functions. The flexibility of the spiking network is demonstrated by showing the average relative errors in the approximation process. A time\u2010 and cost\u2010efficient digital neuromorphic implementation on the base of on\u2010chip learning method for approximating the sigmoid function is also discussed. The paper reports the results of the hardware synthesis and the spiking network's physical implementation on a field\u2010programmable gate array. The maximum frequency and throughput of the implemented network were 83.209\u2009MHz and 9.86\u2009Mb\/s, respectively.<\/jats:p>","DOI":"10.1002\/cta.3075","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T10:41:18Z","timestamp":1624272078000},"page":"2425-2435","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Reconfigurable field\u2010programmable gate array\u2010based on\u2010chip learning neuromorphic digital implementation for nonlinear function approximation"],"prefix":"10.1002","volume":"49","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-6910-1123","authenticated-orcid":false,"given":"Morteza","family":"Gholami","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering Razi University Kermanshah Iran"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7070-5976","authenticated-orcid":false,"given":"Edris","family":"Zaman Farsa","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Islamic Azad University (Sanandaj Branch) Sanandaj Iran"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9323-0312","authenticated-orcid":false,"given":"Gholamreza","family":"Karimi","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering Razi University Kermanshah Iran"}]}],"member":"311","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"SchumanCD PotokTE PattonRM et al.A survey of neuromorphic computing and neural networks in hardware. [Online].2017. Available:https:\/\/arxiv.org\/abs\/1705.06963"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2016.2598413"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2941772"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2933719"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2019.2903009"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2014.2304638"},{"issue":"4","key":"e_1_2_7_9_1","doi-asserted-by":"crossref","first-page":"409","DOI":"10.55782\/ane-2011-1862","article-title":"Introduction to spiking neural networks: information processing, learning and applications","volume":"71","author":"Ponulak F","year":"2011","journal-title":"Acta Neurobiol Exp"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2006.11.029"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00439"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(01)00080-6"},{"key":"e_1_2_7_13_1","first-page":"621","article-title":"Sound classification and function approximation using spiking neural networks","volume":"2005","author":"Amin HH","year":"2005","journal-title":"Int Conf Intell Comput"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2011.05.007"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-007-0140-z"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2005.860850"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2010.2055157"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCAS.2013.2256271"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2016.2618351"},{"issue":"7","key":"e_1_2_7_20_1","first-page":"2651","article-title":"CORDIC\u2010SNN: on\u2010FPGA STDP learning with Izhikevich neurons","volume":"66","author":"Heidarpur M","year":"2019","journal-title":"IEEE Trans Circ Syst"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00090"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2014.00379"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2013.2294916"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10825-015-0709-x"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2017.01.003"},{"key":"e_1_2_7_26_1","first-page":"1","article-title":"Unsupervised character recognition with a simplified FPGA neuromorphic system","author":"Lammie C","year":"2018","journal-title":"Proc IEEE Int Symp Circuits Syst (ISCAS)"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2019.2890846"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2020.2968588"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/cta.2753"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/cta.2457"},{"key":"e_1_2_7_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/cta.2596"},{"key":"e_1_2_7_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/cta.2877"},{"key":"e_1_2_7_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2004.832719"}],"container-title":["International Journal of Circuit Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cta.3075","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cta.3075","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cta.3075","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T07:09:05Z","timestamp":1725260945000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cta.3075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":32,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["10.1002\/cta.3075"],"URL":"https:\/\/doi.org\/10.1002\/cta.3075","archive":["Portico"],"relation":{},"ISSN":["0098-9886","1097-007X"],"issn-type":[{"type":"print","value":"0098-9886"},{"type":"electronic","value":"1097-007X"}],"subject":[],"published":{"date-parts":[[2021,6,21]]},"assertion":[{"value":"2021-02-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}