Antiferromagnetic artificial neuron modeling of the withdrawal reflex
Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological wi...
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| Published in | Journal of computational neuroscience Vol. 52; no. 3; pp. 197 - 206 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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01.08.2024
Springer Nature B.V |
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| Abstract | Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex. |
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| AbstractList | Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex. Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex.Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex. |
| Author | Quach, Lily Louis, Steven Tyberkevych, Vasyl Bradley, Hannah |
| Author_xml | – sequence: 1 givenname: Hannah orcidid: 0000-0001-9208-4827 surname: Bradley fullname: Bradley, Hannah email: hbradley@oakland.edu organization: Department of Physics, Oakland University – sequence: 2 givenname: Lily surname: Quach fullname: Quach, Lily organization: Oakland University William Beaumont School of Medicine – sequence: 3 givenname: Steven orcidid: 0000-0002-6256-6005 surname: Louis fullname: Louis, Steven organization: Department of Electrical and Computer Engineering, Oakland University – sequence: 4 givenname: Vasyl orcidid: 0000-0002-8374-2565 surname: Tyberkevych fullname: Tyberkevych, Vasyl organization: Department of Physics, Oakland University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38987452$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s11571-018-9503-3 10.1113/jphysiol.1952.sp004764 10.1038/nature23011 10.1109/MM.2018.112130359 10.1172/JCI42843 10.1016/j.biosystems.2004.05.007 10.1038/s41928-019-0360-9 10.1103/PhysRevApplied.8.064007 10.1038/nrn1519 10.3390/toxins13040282 10.1080/21663831.2022.2147803 10.1063/5.0003477 10.1088/1361-6528/aa7af5 10.1063/1.5042452 10.34133/2022/9851843 10.1109/URTC51696.2020.9668863 10.1063/1.4977974 10.1016/j.isci.2022.104119 10.1152/jn.00084.2018 10.1103/PhysRevB.103.134431 10.1038/s41565-023-01452-w 10.1063/5.0128530 10.1201/b13510 10.1002/adma.201905399 10.1007/s004220050421 10.1038/s41467-023-36728-1 10.1038/srep30039 10.1039/C7NR09722K 10.1103/PhysRevApplied.11.034015 10.1007/s10827-007-0037-7 10.21203/rs.3.rs-4365235/v1 10.1109/TED.2019.2938952 10.1103/PhysRevApplied.19.064010 10.1590/S0103-97332005000600013 10.1088/1367-2630/17/10/103039 10.1002/adfm.202200241 10.1038/s41598-018-33697-0 10.1038/s41565-019-0593-9 |
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| Keywords | Artificial neuron Biological system modeling Neuroanatomy Antiferromagnets Artificial neural networks |
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| References | BradleyHLouisSTrevillianCQuachLBankowskiESlavinATyberkevychVArtificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computingAIP Advances20231310152061:CAS:528:DC%2BB3sXkvF2luw%3D%3D10.1063/5.0128530 ChenXKangWZhuDZhangXLeiNZhangYZhouYZhaoWA compact skyrmionic leaky-inotetegrate-fire spiking neuron deviceNanoscale20181013613961461:CAS:528:DC%2BC1cXjtFaksLs%3D10.1039/C7NR09722K29557440ISSN 2040-3372. Publisher: The Royal Society of Chemistry LiSKangWHuangYZhangXZhouYZhaoWMagnetic skyrmion-based artificial neuron deviceNanotechnology20172831LT011:CAS:528:DC%2BC1cXitVSqsLrI10.1088/1361-6528/aa7af528639562 BashorDPA large-scale model of some spinal reflex circuitsBiological cybernetics19987821471571:STN:280:DyaK1c7osFOhtQ%3D%3D10.1007/s0042200504219525039 WangDTangRLinHLiuLXuNSunYZhaoXWangZWangDMaiZZhouYGaoNSongCZhuLWuTLiuMXingGSpintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computingNature Communications202314110681:CAS:528:DC%2BB3sXkt1Ohtr0%3D10.1038/s41467-023-36728-1368288569957988ISSN 2041-1723. Number: 1 Publisher: Nature Publishing Group DerderianCShumwayKRTadiPPhysiology2023Withdrawal Response. In StatPearlsStatPearls Publishing, Treasure Island (FL) CaiJFangBZhangLLvWZhangBZhouTFinocchioGZengZVoltage-Controlled Spintronic Stochastic Neuron Based on a Magnetic Tunnel JunctionPhysical Review Applied20191131:CAS:528:DC%2BC1MXhtFGrtb%2FN10.1103/PhysRevApplied.11.034015 TorrejonJRiouMAraujoFATsunegiSKhalsaGQuerliozDBortolottiPCrosVYakushijiKFukushimaAKubotaHYuasaSStilesMDGrollierJNeuromorphic computing with nanoscale spintronic oscillatorsNature201754776644284311:CAS:528:DC%2BC2sXht1ent7bL10.1038/nature23011287489305575904ISSN 1476–4687. Number: 7664 Publisher: Nature Publishing Group KhymynRLisenkovIVoorheisJSulymenkoOProkopenkoOTiberkevichVAkermanJSlavinAUltra-fast artificial neuron: generation of picosecond-duration spikes in a current-driven antiferromagnetic auto-oscillatorScientific Reports201881157271:CAS:528:DC%2BC1MXhtFGgtLg%3D10.1038/s41598-018-33697-0303561046200791ISSN 2045–2322. Number: 1 Publisher: Nature Publishing Group RossALerouxNDe RizAMarkovićDSanz-HernándezDTrastoyJBortolottiPQuerliozDMartinsLBenettiLClaroMSAnacletoPSchulmanATarisTBegueretJBSaïghiSJenkinsASFerreiraRVincentAFMizrahiFAGrollierJMultilayer spintronic neural networks with radiofrequency connectionsNature Nanotechnology202310.1038/s41565-023-01452-w37500772ISSN 1748-3395. Publisher: Nature Publishing Group BaoCKimTHKalhoriAHKimWSA 3D-printed neuromorphic humanoid hand for grasping unknown objectsIscience202210.1016/j.isci.2022.104119366863929852350 CarpenterRReddiBNeurophysiology: a conceptual approach [free web resources, with Vitalsource ebook]20125LondonHodder Arnold10.1201/b13510ISBN 978-1-4441-3517-6 Payra, S., Loke, G., & Fink, Y. (2020). Enabling adaptive robot-environment interaction and context-aware artificial somatosensory reflexes through sensor-embedded Fibers. In 2020 IEEE MIT Undergraduate Research Technology Conference (URTC) (pp. 1–4). https://doi.org/10.1109/URTC51696.2020.9668863 SenguptaAPandaPWijesinghePKimYRoyKMagnetic tunnel junction mimics stochastic cortical spiking neuronsScientific Reports201661300391:CAS:528:DC%2BC2sXksFejtr8%3D10.1038/srep30039274439134956755ISSN 2045-2322. Number: 1 Publisher: Nature Publishing Group DalcinBLCruzFACortezCMPassosEPComputer modeling of a spinal reflex circuitBrazilian Journal of Physics20053598799410.1590/S0103-97332005000600013ISSN 0103–9733, 1678–4448. Publisher: Sociedade Brasileira de Física WangDZhaoSLiLWangLCuiSWangSLouZShenGAll-flexible artificial reflex arc based on threshold-switching memristorAdvanced Functional Materials2022322122002411:CAS:528:DC%2BB38Xkt1antb8%3D10.1002/adfm.202200241ISSN 1616-3028 GrollierJQuerliozDCamsariKYEverschor-SitteKFukamiSStilesMDNeuromorphic spintronicsNature Electronics20203736037010.1038/s41928-019-0360-9ISSN 2520-1131. Number: 7 Publisher: Nature Publishing Group LiuYBarsukovIBarlasYKrivorotovINLakeRKSynthetic antiferromagnet-based spin Josephson oscillatorApplied Physics Letters2020116131324091:CAS:528:DC%2BB3cXmsVShu7w%3D10.1063/5.0003477ISSN 0003-6951 Louis, S., Bradley, H., Slavin, A., & Tyberkevych, V. (2022). Artificial neuron based on a spin torque nano oscillator. Book of Abstracts for 7th International Conference on Magnonics, C4–28. PurvesDAugustineGJFitzpatrickDKatzLCLaMantiaASMcNamaraJOWilliamsSMExcitatory and inhibitory postsynaptic potentials. Neuroscience20012Sinauer Associates ZhuFWangRPanXZhuZEnergy expenditure computation of a single bursting neuronCognitive Neurodynamics2019131758710.1007/s11571-018-9503-330728872ISSN 1871-4080 HodgkinALHuxleyAFA quantitative description of membrane current and its application to conduction and excitation in nerveThe Journal of Physiology195211745005441:STN:280:DyaG3s%2FhsFGjug%3D%3D10.1113/jphysiol.1952.sp004764129912371392413ISSN 0022-3751 HassanNHuXJiang-WeiLBrignerWHAkinolaOGGarcia-SanchezFPasqualeMBennettCHIncorviaJACFriedmanJSMagnetic domain wall neuron with lateral inhibitionJournal of Applied Physics20181241515212710.1063/1.5042452Publisher: AIP Publishing LLC HeKLiuYWangMChenGJiangYYuJWanCQiDXiaoMLeowWRYangHAntoniettiMChenXAn Artificial Somatic Reflex ArcAdvanced Materials202032419053991:CAS:528:DC%2BC1MXitlejt77O10.1002/adma.201905399ISSN 1521–4095 RodriguesDRMoukhaderRLuoYFangBPontlevyAHamadehAZengZCarpentieriMFinocchioGSpintronic Hodgkin-Huxley-Analogue Neuron Implemented with a Single Magnetic Tunnel JunctionPhysical Review Applied20231960640101:CAS:528:DC%2BB3sXhsFGitrbI10.1103/PhysRevApplied.19.064010Publisher: American Physical Society Bradley, H., Louis, S., Slavin, A., & Tyberkevych, V. (2023b). Pattern recognition using spiking antiferromagnetic neurons. arXiv:2308.09071 ThauLReddyVSinghPAnatomy2023Central Nervous System. In StatPearlsStatPearls Publishing, Treasure Island (FL) Sulymenko, O.R., Prokopenko, O.V., Tiberkevich, V.S., Slavin, A.N., Ivanov, B.A., & Khymyn, R.S. (2017). THz-Frequency Spin-Hall Auto-Oscillator Based on a Canted Antiferromagnet. Physical Review Applied, 8(6):064007. https://doi.org/10.1103/PhysRevApplied.8.064007. arXiv:1707.07491. ISSN 2331-7019. BrignerWHFriedmanJSHassanNJiang-WeiLHuXSahaDBennettCHMarinellaMJIncorviaJACGarcia-SanchezFShape-based magnetic domain wall drift for an artificial spintronic leaky integrate-and-fire neuronIEEE Transactions on Electron Devices20196611497049751:CAS:528:DC%2BB3cXpvVOms7s%3D10.1109/TED.2019.2938952 SalinasRIChenPCYangCYLaiCHSpintronic materials and devices towards an artificial neural network: accomplishments and the last mileMaterials Research Letters20231153053261:CAS:528:DC%2BB38XjtVClsbbE10.1080/21663831.2022.2147803ISSN null. Publisher: Taylor & Francis _eprint DanielsMWGuoWStocksGMXiaoDXiaoJSpin-transfer torque induced spin waves in antiferromagnetic insulatorsNew Journal of Physics201517101030391:CAS:528:DC%2BC28XhtV2ls7zJ10.1088/1367-2630/17/10/103039ISSN 1367-2630 ConsoloGValentiGSafinARNikitovSATyberkevichVSlavinATheory of the electric field controlled antiferromagnetic spin Hall oscillator and detectorPhysical Review B2021103131344311:CAS:528:DC%2BB3MXhtVyjtLnI10.1103/PhysRevB.103.134431Publisher: American Physical Society DubinAEPatapoutianANociceptors: the sensors of the pain pathwayThe Journal of Clinical Investigation201012011376037721:CAS:528:DC%2BC3cXhsVWgtb3P10.1172/JCI42843210419582964977ISSN 0021-9738 DaviesMSrinivasaNLinTHChinyaGJoshiPLinesAWildAWangHMathaikuttyDLoihi: a neuromorphic manycore processor with on-chip learningIEEE Micro201810.1109/MM.2018.112130359 FolgheraiterMGiniGHuman-like reflex control for an artificial handBiosystems2004761657410.1016/j.biosystems.2004.05.00715351131ISSN 0303-2647 KhymynRTiberkevichVSlavinAAntiferromagnetic spin current rectifierAIP Advances2017750559311:CAS:528:DC%2BC2sXjslyksbY%3D10.1063/1.4977974ISSN 2158-3226 StienenAHASchoutenACSchuurmansJvan der HelmFCTAnalysis of reflex modulation with a biologically realistic neural networkJournal of Computational Neuroscience200723333334810.1007/s10827-007-0037-7175031692799624ISSN 1573-6873 MarkramHToledo-RodriguezMWangYGuptaASilberbergGWuCInterneurons of the neocortical inhibitory systemNature Reviews Neuroscience20045107938071:CAS:528:DC%2BD2cXns1Kiurc%3D10.1038/nrn151915378039ISSN 1471–0048. Number: 10 Publisher: Nature Publishing Group GangulyJKulshreshthaDAlmotiriMJogMMuscle tone physiology and abnormalitiesToxins20211342821:CAS:528:DC%2BB3MXhtlCrtbjE10.3390/toxins13040282339233978071570ISSN 2072-6651 ZahedinejadMAwadAAMuralidharSKhymynRFularaHMazraatiHDvornikMÅkermanJTwo-dimensional mutually synchronized spin Hall nano-oscillator arrays for neuromorphic computingNature Nanotechnology202015147521:CAS:528:DC%2BB3cXjvF2huw%3D%3D10.1038/s41565-019-0593-931873287ISSN 1748-3395. Number: 1 Publisher: Nature Publishing Group SunLDuYYuHWeiHXuWXuWAn artificial reflex arc that perceives afferent visual and tactile information and controls efferent muscular actionsResearch202210.34133/2022/98518433645243311030818ISSN 2639-5274 BaarsBJGageNMCognition, brain, and consciousness: Introduction to cognitive neuroscience2010Academic Press Disability Institute of Medicine (US) Committee on Pain, Osterweis, M., Kleinman, A., & Mechanic, D. (1987). The Anatomy and Physiology of Pain. Publication Title: Pain and Disability: Clinical, Behavioral, and Public Policy Perspectives. US: National Academies Press. LatashMLMuscle coactivation: definitions, mechanisms, and functionsJournal of Neurophysiology201812018810410.1152/jn.00084.2018295898126093955ISSN 0022-3077. 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| References_xml | – volume: 13 start-page: 75 issue: 1 year: 2019 ident: 873_CR44 publication-title: Cognitive Neurodynamics doi: 10.1007/s11571-018-9503-3 contributor: fullname: F Zhu – volume: 117 start-page: 500 issue: 4 year: 1952 ident: 873_CR22 publication-title: The Journal of Physiology doi: 10.1113/jphysiol.1952.sp004764 contributor: fullname: AL Hodgkin – volume: 547 start-page: 428 issue: 7664 year: 2017 ident: 873_CR40 publication-title: Nature doi: 10.1038/nature23011 contributor: fullname: J Torrejon – year: 2018 ident: 873_CR13 publication-title: IEEE Micro doi: 10.1109/MM.2018.112130359 contributor: fullname: M Davies – volume: 120 start-page: 3760 issue: 11 year: 2010 ident: 873_CR16 publication-title: The Journal of Clinical Investigation doi: 10.1172/JCI42843 contributor: fullname: AE Dubin – volume: 76 start-page: 65 issue: 1 year: 2004 ident: 873_CR17 publication-title: Biosystems doi: 10.1016/j.biosystems.2004.05.007 contributor: fullname: M Folgheraiter – volume: 3 start-page: 360 issue: 7 year: 2020 ident: 873_CR19 publication-title: Nature Electronics doi: 10.1038/s41928-019-0360-9 contributor: fullname: J Grollier – volume-title: Excitatory and inhibitory postsynaptic potentials. Neuroscience year: 2001 ident: 873_CR31 contributor: fullname: D Purves – ident: 873_CR37 doi: 10.1103/PhysRevApplied.8.064007 – ident: 873_CR15 – volume: 5 start-page: 793 issue: 10 year: 2004 ident: 873_CR29 publication-title: Nature Reviews Neuroscience doi: 10.1038/nrn1519 contributor: fullname: H Markram – volume: 13 start-page: 282 issue: 4 year: 2021 ident: 873_CR18 publication-title: Toxins doi: 10.3390/toxins13040282 contributor: fullname: J Ganguly – volume: 11 start-page: 305 issue: 5 year: 2023 ident: 873_CR34 publication-title: Materials Research Letters doi: 10.1080/21663831.2022.2147803 contributor: fullname: RI Salinas – volume: 116 start-page: 132409 issue: 13 year: 2020 ident: 873_CR27 publication-title: Applied Physics Letters doi: 10.1063/5.0003477 contributor: fullname: Y Liu – volume: 28 start-page: 31LT01 year: 2017 ident: 873_CR26 publication-title: Nanotechnology doi: 10.1088/1361-6528/aa7af5 contributor: fullname: S Li – volume: 124 start-page: 152127 issue: 15 year: 2018 ident: 873_CR20 publication-title: Journal of Applied Physics doi: 10.1063/1.5042452 contributor: fullname: N Hassan – year: 2022 ident: 873_CR38 publication-title: Research doi: 10.34133/2022/9851843 contributor: fullname: L Sun – ident: 873_CR30 doi: 10.1109/URTC51696.2020.9668863 – volume: 7 start-page: 055931 issue: 5 year: 2017 ident: 873_CR23 publication-title: AIP Advances doi: 10.1063/1.4977974 contributor: fullname: R Khymyn – year: 2022 ident: 873_CR2 publication-title: Iscience doi: 10.1016/j.isci.2022.104119 contributor: fullname: C Bao – volume: 120 start-page: 88 issue: 1 year: 2018 ident: 873_CR25 publication-title: Journal of Neurophysiology doi: 10.1152/jn.00084.2018 contributor: fullname: ML Latash – volume: 103 start-page: 134431 issue: 13 year: 2021 ident: 873_CR10 publication-title: Physical Review B doi: 10.1103/PhysRevB.103.134431 contributor: fullname: G Consolo – year: 2023 ident: 873_CR33 publication-title: Nature Nanotechnology doi: 10.1038/s41565-023-01452-w contributor: fullname: A Ross – volume-title: Anatomy year: 2023 ident: 873_CR39 contributor: fullname: L Thau – volume: 13 start-page: 015206 issue: 1 year: 2023 ident: 873_CR5 publication-title: AIP Advances doi: 10.1063/5.0128530 contributor: fullname: H Bradley – volume-title: Neurophysiology: a conceptual approach [free web resources, with Vitalsource ebook] year: 2012 ident: 873_CR8 doi: 10.1201/b13510 contributor: fullname: R Carpenter – volume: 32 start-page: 1905399 issue: 4 year: 2020 ident: 873_CR21 publication-title: Advanced Materials doi: 10.1002/adma.201905399 contributor: fullname: K He – volume-title: Physiology year: 2023 ident: 873_CR14 contributor: fullname: C Derderian – volume: 78 start-page: 147 issue: 2 year: 1998 ident: 873_CR3 publication-title: Biological cybernetics doi: 10.1007/s004220050421 contributor: fullname: DP Bashor – volume: 14 start-page: 1068 issue: 1 year: 2023 ident: 873_CR42 publication-title: Nature Communications doi: 10.1038/s41467-023-36728-1 contributor: fullname: D Wang – volume: 6 start-page: 30039 issue: 1 year: 2016 ident: 873_CR35 publication-title: Scientific Reports doi: 10.1038/srep30039 contributor: fullname: A Sengupta – volume: 10 start-page: 6139 issue: 13 year: 2018 ident: 873_CR9 publication-title: Nanoscale doi: 10.1039/C7NR09722K contributor: fullname: X Chen – volume: 11 issue: 3 year: 2019 ident: 873_CR7 publication-title: Physical Review Applied doi: 10.1103/PhysRevApplied.11.034015 contributor: fullname: J Cai – volume: 23 start-page: 333 issue: 3 year: 2007 ident: 873_CR36 publication-title: Journal of Computational Neuroscience doi: 10.1007/s10827-007-0037-7 contributor: fullname: AHA Stienen – ident: 873_CR4 doi: 10.21203/rs.3.rs-4365235/v1 – volume: 66 start-page: 4970 issue: 11 year: 2019 ident: 873_CR6 publication-title: IEEE Transactions on Electron Devices doi: 10.1109/TED.2019.2938952 contributor: fullname: WH Brigner – ident: 873_CR28 – volume: 19 start-page: 064010 issue: 6 year: 2023 ident: 873_CR32 publication-title: Physical Review Applied doi: 10.1103/PhysRevApplied.19.064010 contributor: fullname: DR Rodrigues – volume: 35 start-page: 987 year: 2005 ident: 873_CR11 publication-title: Brazilian Journal of Physics doi: 10.1590/S0103-97332005000600013 contributor: fullname: BL Dalcin – volume: 17 start-page: 103039 issue: 10 year: 2015 ident: 873_CR12 publication-title: New Journal of Physics doi: 10.1088/1367-2630/17/10/103039 contributor: fullname: MW Daniels – volume: 32 start-page: 2200241 issue: 21 year: 2022 ident: 873_CR41 publication-title: Advanced Functional Materials doi: 10.1002/adfm.202200241 contributor: fullname: D Wang – volume: 8 start-page: 15727 issue: 1 year: 2018 ident: 873_CR24 publication-title: Scientific Reports doi: 10.1038/s41598-018-33697-0 contributor: fullname: R Khymyn – volume-title: Cognition, brain, and consciousness: Introduction to cognitive neuroscience year: 2010 ident: 873_CR1 contributor: fullname: BJ Baars – volume: 15 start-page: 47 issue: 1 year: 2020 ident: 873_CR43 publication-title: Nature Nanotechnology doi: 10.1038/s41565-019-0593-9 contributor: fullname: M Zahedinejad |
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| SubjectTerms | Antiferromagnetism Artificial neural networks Biological effects Biomedical and Life Sciences Biomedicine Effectiveness Human Genetics Interneurons Modelling Motor neurons Neural networks Neurology Neurons Neurosciences Pain perception Sensory neurons Sensory stimuli Spin dynamics Spinal cord Stimuli Theory of Computation |
| Title | Antiferromagnetic artificial neuron modeling of the withdrawal reflex |
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