{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:47:35Z","timestamp":1776887255382,"version":"3.51.2"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52077056"],"award-info":[{"award-number":["52077056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976240"],"award-info":[{"award-number":["61976240"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2022YFC2402203"],"award-info":[{"award-number":["2022YFC2402203"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s00521-024-09667-1","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T20:29:32Z","timestamp":1713817772000},"page":"12505-12527","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["FPGA-based small-world spiking neural network with anti-interference ability under external noise"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3427-8222","authenticated-orcid":false,"given":"Lei","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongkang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youxi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guizhi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"9667_CR1","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1038\/s41566-020-00754-y","volume":"15","author":"BJ Shastri","year":"2021","unstructured":"Shastri BJ, Tait AN, Ferreira De Lima T et al (2021) Photonics for artificial intelligence and neuromorphic computing. Nate Photon 15:102\u2013114. https:\/\/doi.org\/10.1038\/s41566-020-00754-y","journal-title":"Nate Photon"},{"key":"9667_CR2","doi-asserted-by":"publisher","first-page":"13479","DOI":"10.1007\/s00521-020-04755-4","volume":"32","author":"L Qu","year":"2020","unstructured":"Qu L, Zhao Z, Wang L et al (2020) Efficient and hardware-friendly methods to implement competitive learning for spiking neural networks. Neural Comput Appl 32:13479\u201313490. https:\/\/doi.org\/10.1007\/s00521-020-04755-4","journal-title":"Neural Comput Appl"},{"key":"9667_CR3","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.neuroimage.2019.03.078","volume":"195","author":"MS Sherwood","year":"2019","unstructured":"Sherwood MS, Parker JG, Diller EE et al (2019) Self-directed down-regulation of auditory cortex activity mediated by real-time fMRI neurofeedback augments attentional processes, resting cerebral perfusion, and auditory activation. Neuroimage 195:475\u2013489. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.03.078","journal-title":"Neuroimage"},{"key":"9667_CR4","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s11571-021-09689-8","volume":"16","author":"C Luo","year":"2022","unstructured":"Luo C, Li F, Li P et al (2022) A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 16:17\u201341. https:\/\/doi.org\/10.1007\/s11571-021-09689-8","journal-title":"Cogn Neurodyn"},{"key":"9667_CR5","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1109\/TNNLS.2020.3041624","volume":"33","author":"Q Hong","year":"2022","unstructured":"Hong Q, Chen H, Sun J et al (2022) Memristive circuit implementation of a self-repairing network based on biological astrocytes in robot application. IEEE Trans Neural Netw Learn Syst 33:2106\u20132120. https:\/\/doi.org\/10.1109\/TNNLS.2020.3041624","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9667_CR6","doi-asserted-by":"publisher","first-page":"15649","DOI":"10.1007\/s00521-022-07220-6","volume":"34","author":"FM Quintana","year":"2022","unstructured":"Quintana FM, Perez-Pe\u00f1a F, Galindo PL (2022) Bio-plausible digital implementation of a reward modulated STDP synapse. Neural Comput Appl 34:15649\u201315660. https:\/\/doi.org\/10.1007\/s00521-022-07220-6","journal-title":"Neural Comput Appl"},{"key":"9667_CR7","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TCDS.2019.2918228","volume":"13","author":"L Cheng","year":"2021","unstructured":"Cheng L, Liu Y, Hou ZG et al (2021) A rapid spiking neural network approach with an application on hand gesture recognition. IEEE Trans Cognit Dev Syst 13:151\u2013161. https:\/\/doi.org\/10.1109\/TCDS.2019.2918228","journal-title":"IEEE Trans Cognit Dev Syst"},{"key":"9667_CR8","doi-asserted-by":"publisher","first-page":"12317","DOI":"10.1007\/s00521-021-05832-y","volume":"33","author":"SG Hu","year":"2021","unstructured":"Hu SG, Qiao GC, Chen TP et al (2021) Quantized STDP-based online-learning spiking neural network. Neural Comput Appl 33:12317\u201312332. https:\/\/doi.org\/10.1007\/s00521-021-05832-y","journal-title":"Neural Comput Appl"},{"key":"9667_CR9","doi-asserted-by":"publisher","first-page":"2003610","DOI":"10.1002\/adma.202003610","volume":"32","author":"J Yang","year":"2020","unstructured":"Yang J, Wang R, Ren Y et al (2020) Neuromorphic engineering: from biological to spike-based hardware nervous systems. Adv Mater 32:2003610. https:\/\/doi.org\/10.1002\/adma.202003610","journal-title":"Adv Mater"},{"key":"9667_CR10","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1109\/TCSI.2020.3003769","volume":"67","author":"D Valencia","year":"2020","unstructured":"Valencia D, Fard SF, Alimohammad A (2020) An artificial neural network processor with a custom instruction set architecture for embedded applications. IEEE Trans Circuits Syst I Regul Pap 67:5200\u20135210. https:\/\/doi.org\/10.1109\/TCSI.2020.3003769","journal-title":"IEEE Trans Circuits Syst I Regul Pap"},{"key":"9667_CR11","doi-asserted-by":"publisher","first-page":"2272","DOI":"10.3390\/electronics10182272","volume":"10","author":"S Bouguezzi","year":"2021","unstructured":"Bouguezzi S, Fredj HB, Belabed T et al (2021) An efficient FPGA-Based convolutional neural network for classification: Ad-MobileNet. Electronics 10:2272. https:\/\/doi.org\/10.3390\/electronics10182272","journal-title":"Electronics"},{"key":"9667_CR12","doi-asserted-by":"publisher","first-page":"2733","DOI":"10.1109\/TVLSI.2014.2365458","volume":"23","author":"Y Kim","year":"2015","unstructured":"Kim Y, Zhang Y, Li P (2015) Energy efficient approximate arithmetic for error resilient neuromorphic computing. IEEE Trans Very Large Scale Integr (VLSI) Syst 23:2733\u20132737. https:\/\/doi.org\/10.1109\/TVLSI.2014.2365458","journal-title":"IEEE Trans Very Large Scale Integr (VLSI) Syst"},{"key":"9667_CR13","doi-asserted-by":"publisher","first-page":"101839","DOI":"10.1016\/j.sysarc.2020.101839","volume":"112","author":"S Mittal","year":"2021","unstructured":"Mittal S, Umesh S (2021) A survey on hardware accelerators and optimization techniques for RNNs. J Syst Architect 112:101839. https:\/\/doi.org\/10.1016\/j.sysarc.2020.101839","journal-title":"J Syst Architect"},{"key":"9667_CR14","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.1109\/TCSI.2022.3160693","volume":"69","author":"Y Liu","year":"2022","unstructured":"Liu Y, Chen Y, Ye W et al (2022) FPGA-NHAP: a general fpga-based neuromorphic hardware acceleration platform with high speed and low power. IEEE Trans Circuits Syst I Regul Pap 69:2553\u20132566. https:\/\/doi.org\/10.1109\/TCSI.2022.3160693","journal-title":"IEEE Trans Circuits Syst I Regul Pap"},{"key":"9667_CR15","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.1109\/TCSI.2022.3145016","volume":"69","author":"C Ding","year":"2022","unstructured":"Ding C, Huan Y, Jia H et al (2022) A hybrid-mode on-chip router for the large-scale FPGA-based neuromorphic platform. IEEE Trans Circuits Syst I Regul Pap 69:1990\u20132001. https:\/\/doi.org\/10.1109\/TCSI.2022.3145016","journal-title":"IEEE Trans Circuits Syst I Regul Pap"},{"key":"9667_CR16","doi-asserted-by":"publisher","first-page":"17821","DOI":"10.1007\/s00521-023-08650-6","volume":"35","author":"D Valencia","year":"2023","unstructured":"Valencia D, Alimohammad A (2023) A generalized hardware architecture for real-time spiking neural networks. Neural Comput Appl 35:17821\u201317835. https:\/\/doi.org\/10.1007\/s00521-023-08650-6","journal-title":"Neural Comput Appl"},{"key":"9667_CR17","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.1152\/jn.00686.2005","volume":"94","author":"R Brette","year":"2005","unstructured":"Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94:3637\u20133642. https:\/\/doi.org\/10.1152\/jn.00686.2005","journal-title":"J Neurophysiol"},{"key":"9667_CR18","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1113\/jphysiol.1952.sp004764","volume":"117","author":"AL Hodgkin","year":"1952","unstructured":"Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500\u2013544. https:\/\/doi.org\/10.1113\/jphysiol.1952.sp004764","journal-title":"J Physiol"},{"key":"9667_CR19","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","volume":"14","author":"E Izhikevich","year":"2003","unstructured":"Izhikevich E (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14:1569\u20131572. https:\/\/doi.org\/10.1109\/TNN.2003.820440","journal-title":"IEEE Trans Neural Netw"},{"key":"9667_CR20","doi-asserted-by":"publisher","first-page":"036008","DOI":"10.1088\/1748-3190\/abedce","volume":"16","author":"O Zahra","year":"2021","unstructured":"Zahra O, Tolu S, Navarro-Alarcon D (2021) Differential mapping spiking neural network for sensor-based robot control. Bioinspiration Biomim 16:036008. https:\/\/doi.org\/10.1088\/1748-3190\/abedce","journal-title":"Bioinspiration Biomim"},{"key":"9667_CR21","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3389\/fnins.2017.00090","volume":"11","author":"D Pani","year":"2017","unstructured":"Pani D, Meloni P, Tuveri G et al (2017) An FPGA platform for real-time simulation of spiking neuronal networks. Front Neurosci 11:90. https:\/\/doi.org\/10.3389\/fnins.2017.00090","journal-title":"Front Neurosci"},{"key":"9667_CR22","doi-asserted-by":"publisher","unstructured":"Hornberger G, Wiberg P (2005) Numerical methods in the hydrological sciences. American Geophysical Union, Washington D. C. https:\/\/doi.org\/10.1002\/9781118709528","DOI":"10.1002\/9781118709528"},{"key":"9667_CR23","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1007\/s11071-021-06704-9","volume":"105","author":"K Xu","year":"2021","unstructured":"Xu K, Maidana JP, Orio P (2021) Diversity of neuronal activity is provided by hybrid synapses. Nonlinear Dyn 105:2693\u20132710. https:\/\/doi.org\/10.1007\/s11071-021-06704-9","journal-title":"Nonlinear Dyn"},{"key":"9667_CR24","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.bbrc.2021.10.003","volume":"582","author":"N Koganezawa","year":"2021","unstructured":"Koganezawa N, Hanamura K, Schwark M et al (2021) Super-resolved 3D-STED microscopy identifies a layer-specific increase in excitatory synapses in the hippocampal CA1 region of Neuroligin-3 KO mice. Biochem Biophys Res Commun 582:144\u2013149. https:\/\/doi.org\/10.1016\/j.bbrc.2021.10.003","journal-title":"Biochem Biophys Res Commun"},{"key":"9667_CR25","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1109\/TBCAS.2019.2945406","volume":"13","author":"H Tang","year":"2019","unstructured":"Tang H, Kim H, Kim H et al (2019) Spike counts based low complexity SNN architecture with binary synapse. IEEE Trans Biomed Circuits Syst 13:1664\u20131677. https:\/\/doi.org\/10.1109\/TBCAS.2019.2945406","journal-title":"IEEE Trans Biomed Circuits Syst"},{"key":"9667_CR26","doi-asserted-by":"publisher","unstructured":"Xue F, Hang Guan, Li X (2016) Improving liquid state machine with hybrid plasticity. In: 2016 IEEE advanced information management, communicates, electronic and automation control conference (IMCEC), pp 1955\u20131959. https:\/\/doi.org\/10.1109\/IMCEC.2016.7867559","DOI":"10.1109\/IMCEC.2016.7867559"},{"key":"9667_CR27","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2019.11.045","volume":"382","author":"G Zhang","year":"2020","unstructured":"Zhang G, Li B, Wu J et al (2020) A low-cost and high-speed hardware implementation of spiking neural network. Neurocomputing 382:106\u2013115. https:\/\/doi.org\/10.1016\/j.neucom.2019.11.045","journal-title":"Neurocomputing"},{"key":"9667_CR28","doi-asserted-by":"publisher","unstructured":"Lammie C, Hamilton T, Azghadi MR (2018) Unsupervised character recognition with a simplified FPGA neuromorphic system. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp 1\u20135. https:\/\/doi.org\/10.1109\/ISCAS.2018.8351532","DOI":"10.1109\/ISCAS.2018.8351532"},{"key":"9667_CR29","doi-asserted-by":"publisher","first-page":"321","DOI":"10.3390\/math11020321","volume":"11","author":"D Chung","year":"2023","unstructured":"Chung D, Sohn I (2023) Neural network optimization based on complex network theory: a survey. Mathematics 11:321. https:\/\/doi.org\/10.3390\/math11020321","journal-title":"Mathematics"},{"key":"9667_CR30","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.physa.2017.10.003","volume":"492","author":"Z Li","year":"2018","unstructured":"Li Z, Ren T, Xu Y et al (2018) The relationship between synchronization and percolation for regular networks. Physica A 492:375\u2013381. https:\/\/doi.org\/10.1016\/j.physa.2017.10.003","journal-title":"Physica A"},{"key":"9667_CR31","doi-asserted-by":"publisher","first-page":"1950009","DOI":"10.1142\/S0129183119500098","volume":"30","author":"H Lin","year":"2019","unstructured":"Lin H, Wang J (2019) Percolation of a random network by statistical physics method. Int J Mod Phys C 30:1950009. https:\/\/doi.org\/10.1142\/S0129183119500098","journal-title":"Int J Mod Phys C"},{"key":"9667_CR32","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/30918","volume":"393","author":"DJ Watts","year":"1998","unstructured":"Watts DJ, Strogatz SH (1998) Collective dynamics of \u2018small-world\u2019 networks. Nature 393:440\u2013442. https:\/\/doi.org\/10.1038\/30918","journal-title":"Nature"},{"key":"9667_CR33","doi-asserted-by":"publisher","first-page":"3747","DOI":"10.1073\/pnas.0400087101","volume":"101","author":"A Barrat","year":"2004","unstructured":"Barrat A, Barth\u00e9lemy M, Pastor-Satorras R et al (2004) The architecture of complex weighted networks. Proc Natl Acad Sci 101:3747\u20133752. https:\/\/doi.org\/10.1073\/pnas.0400087101","journal-title":"Proc Natl Acad Sci"},{"key":"9667_CR34","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s00406-018-0977-0","volume":"270","author":"A Lubeiro","year":"2020","unstructured":"Lubeiro A, Fatj\u00f3-Vilas M, Guardiola M et al (2020) Analysis of KCNH2 and CACNA1C schizophrenia risk genes on EEG functional network modulation during an auditory odd-ball task. Eur Arch Psychiatry Clin Neurosci 270:433\u2013442. https:\/\/doi.org\/10.1007\/s00406-018-0977-0","journal-title":"Eur Arch Psychiatry Clin Neurosci"},{"key":"9667_CR35","doi-asserted-by":"publisher","first-page":"116702","DOI":"10.1016\/j.jns.2020.116702","volume":"411","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Ren J, Qin Y et al (2020) Altered topological organization of functional brain networks in drug-naive patients with paroxysmal kinesigenic dyskinesia. J Neurol Sci 411:116702. https:\/\/doi.org\/10.1016\/j.jns.2020.116702","journal-title":"J Neurol Sci"},{"key":"9667_CR36","doi-asserted-by":"publisher","first-page":"203","DOI":"10.3389\/fnagi.2020.00203","volume":"12","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Lu T, Xie C et al (2020) Functional disorganization of small-world brain networks in patients with ischemic leukoaraiosis. Front Aging Neurosci 12:203. https:\/\/doi.org\/10.3389\/fnagi.2020.00203","journal-title":"Front Aging Neurosci"},{"key":"9667_CR37","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neunet.2019.01.002","volume":"112","author":"Y Kawai","year":"2019","unstructured":"Kawai Y, Park J, Asada M (2019) A small-world topology enhances the echo state property and signal propagation in reservoir computing. Neural Netw 112:15\u201323. https:\/\/doi.org\/10.1016\/j.neunet.2019.01.002","journal-title":"Neural Netw"},{"key":"9667_CR38","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.neucom.2020.07.111","volume":"418","author":"L Guo","year":"2020","unstructured":"Guo L, Hou L, Wu Y et al (2020) Encoding specificity of scale-free spiking neural network under different external stimulations. Neurocomputing 418:126\u2013138. https:\/\/doi.org\/10.1016\/j.neucom.2020.07.111","journal-title":"Neurocomputing"},{"key":"9667_CR39","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1109\/TSMC.2018.2825021","volume":"49","author":"S Wen","year":"2019","unstructured":"Wen S, Hu R, Yang Y et al (2019) Memristor-based echo state network with online least mean square. IEEE Trans Syst Man Cybern: Syst 49:1787\u20131796. https:\/\/doi.org\/10.1109\/TSMC.2018.2825021","journal-title":"IEEE Trans Syst Man Cybern: Syst"},{"key":"9667_CR40","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.physa.2016.01.052","volume":"451","author":"B Deng","year":"2016","unstructured":"Deng B, Zhu Z, Yang S et al (2016) FPGA implementation of motifs-based neuronal network and synchronization analysis. Physica A 451:388\u2013402. https:\/\/doi.org\/10.1016\/j.physa.2016.01.052","journal-title":"Physica A"},{"key":"9667_CR41","doi-asserted-by":"publisher","first-page":"3063","DOI":"10.1093\/brain\/aww194","volume":"139","author":"H Aerts","year":"2016","unstructured":"Aerts H, Fias W, Caeyenberghs K et al (2016) Brain networks under attack: robustness properties and the impact of lesions. Brain 139:3063\u20133083. https:\/\/doi.org\/10.1093\/brain\/aww194","journal-title":"Brain"},{"key":"9667_CR42","doi-asserted-by":"publisher","first-page":"802606","DOI":"10.3389\/fpsyt.2022.802606","volume":"13","author":"PR Steffen","year":"2022","unstructured":"Steffen PR, Hedges D, Matheson R (2022) The brain is adaptive not triune: how the brain responds to threat, challenge, and change. Front Psych 13:802606. https:\/\/doi.org\/10.3389\/fpsyt.2022.802606","journal-title":"Front Psych"},{"key":"9667_CR43","doi-asserted-by":"publisher","first-page":"18073","DOI":"10.1038\/s41598-021-97314-3","volume":"11","author":"R Krause","year":"2021","unstructured":"Krause R, Van Bavel JJA, Wu C et al (2021) Robust neuromorphic coupled oscillators for adaptive pacemakers. Sci Rep 11:18073. https:\/\/doi.org\/10.1038\/s41598-021-97314-3","journal-title":"Sci Rep"},{"key":"9667_CR44","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1109\/TCSI.2021.3060798","volume":"68","author":"T Tao","year":"2021","unstructured":"Tao T, Ma H, Chen Q et al (2021) Circuit modeling for RRAM-based neuromorphic chip crossbar array with and without write-verify scheme. IEEE Trans Circuits Syst I Regul Pap 68:1906\u20131916. https:\/\/doi.org\/10.1109\/TCSI.2021.3060798","journal-title":"IEEE Trans Circuits Syst I Regul Pap"},{"key":"9667_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMAG.2020.3013258","volume":"57","author":"D Liu","year":"2021","unstructured":"Liu D, Guo L, Wu Y et al (2021) Antiinterference function of scale-free spiking neural network under AC magnetic field stimulation. IEEE Trans Magn 57:1\u20135. https:\/\/doi.org\/10.1109\/TMAG.2020.3013258","journal-title":"IEEE Trans Magn"},{"key":"9667_CR46","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/TNN.2004.832719","volume":"15","author":"E Izhikevich","year":"2004","unstructured":"Izhikevich E (2004) Which model to use for cortical spiking neurons? IEEE Trans Neural Netw 15:1063\u20131070. https:\/\/doi.org\/10.1109\/TNN.2004.832719","journal-title":"IEEE Trans Neural Netw"},{"key":"9667_CR47","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.neuroscience.2016.08.027","volume":"335","author":"R Kobayashi","year":"2016","unstructured":"Kobayashi R, Nishimaru H, Nishijo H (2016) Estimation of excitatory and inhibitory synaptic conductance variations in motoneurons during locomotor-like rhythmic activity. Neuroscience 335:72\u201381. https:\/\/doi.org\/10.1016\/j.neuroscience.2016.08.027","journal-title":"Neuroscience"},{"key":"9667_CR48","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1038\/78829","volume":"3","author":"S Song","year":"2000","unstructured":"Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3:919\u2013926. https:\/\/doi.org\/10.1038\/78829","journal-title":"Nat Neurosci"},{"key":"9667_CR49","doi-asserted-by":"publisher","first-page":"e1007974","DOI":"10.1371\/journal.pcbi.1007974","volume":"16","author":"BG Fenyves","year":"2020","unstructured":"Fenyves BG, Szil\u00e1gyi GS, Vassy Z et al (2020) Synaptic polarity and sign-balance prediction using gene expression data in the caenorhabditis elegans chemical synapse neuronal connectome network. PLoS Comput Biol 16:e1007974. https:\/\/doi.org\/10.1371\/journal.pcbi.1007974","journal-title":"PLoS Comput Biol"},{"key":"9667_CR50","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.brs.2009.03.007","volume":"2","author":"T Radman","year":"2009","unstructured":"Radman T, Ramos RL, Brumberg JC et al (2009) Role of cortical cell type and morphology in subthreshold and suprathreshold uniform electric field stimulation in vitro. Brain Stimul 2:215-228.e3. https:\/\/doi.org\/10.1016\/j.brs.2009.03.007","journal-title":"Brain Stimul"},{"key":"9667_CR51","doi-asserted-by":"publisher","first-page":"083128","DOI":"10.1063\/5.0056672","volume":"31","author":"AS Reis","year":"2021","unstructured":"Reis AS, Brugnago EL, Caldas IL et al (2021) Suppression of chaotic bursting synchronization in clustered scale-free networks by an external feedback signal. Chaos 31:083128. https:\/\/doi.org\/10.1063\/5.0056672","journal-title":"Chaos"},{"key":"9667_CR52","doi-asserted-by":"publisher","first-page":"509075","DOI":"10.3389\/fnhum.2020.509075","volume":"14","author":"A Tetereva","year":"2020","unstructured":"Tetereva A, Kartashov S, Ivanitsky A et al (2020) Variance and scale-free properties of resting-state blood oxygenation level-dependent signal after fear memory acquisition and extinction. Front Hum Neurosci 14:509075. https:\/\/doi.org\/10.3389\/fnhum.2020.509075","journal-title":"Front Hum Neurosci"},{"key":"9667_CR53","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1109\/TNN.2010.2066989","volume":"21","author":"J Piersa","year":"2010","unstructured":"Piersa J, Piekniewski F, Schreiber T (2010) Theoretical model for mesoscopic-level scale-free self-organization of functional brain networks. IEEE Trans Neural Netw 21:1747\u20131758. https:\/\/doi.org\/10.1109\/TNN.2010.2066989","journal-title":"IEEE Trans Neural Netw"},{"key":"9667_CR54","doi-asserted-by":"publisher","unstructured":"Lyon R (1982) A computational model of filtering, detection, and compression in the cochlea. In: ICASSP \u201982. IEEE international conference on acoustics, speech, and signal processing, pp 1282\u20131285. https:\/\/doi.org\/10.1109\/ICASSP.1982.1171644","DOI":"10.1109\/ICASSP.1982.1171644"},{"key":"9667_CR55","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1109\/IJCNN.2003.1224019","volume":"2003","author":"B Schrauwen","year":"2003","unstructured":"Schrauwen B, Van Campenhout J (2003) BSA, a fast and accurate spike train encoding scheme. Proc Int Jt Conf Neural Netw 2003:2825\u20132830. https:\/\/doi.org\/10.1109\/IJCNN.2003.1224019","journal-title":"Proc Int Jt Conf Neural Netw"},{"key":"9667_CR56","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1162\/neco.2009.11-08-901","volume":"22","author":"F Ponulak","year":"2010","unstructured":"Ponulak F, Kasi\u0144ski A (2010) Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting. Neural Comput 22:467\u2013510. https:\/\/doi.org\/10.1162\/neco.2009.11-08-901","journal-title":"Neural Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09667-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09667-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09667-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T14:17:42Z","timestamp":1720448262000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09667-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,22]]},"references-count":56,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["9667"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09667-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,22]]},"assertion":[{"value":"12 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare they have no financial interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declared that we have independently written programs to construct our network and performed the research and analysis of the anti-interference ability of our network.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}