Firing pattern transition of fractional-order memristor-coupled Hindmarsh–Rose neurons model and its medical image encryption for region of interest
Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ding, Dawei [verfasserIn] Chen, Siqi [verfasserIn] Zhang, Hongwei [verfasserIn] Yang, Zongli [verfasserIn] Jin, Fan [verfasserIn] Liu, Xiang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Nonlinear dynamics - Springer Netherlands, 1990, 112(2024), 12 vom: 21. Apr., Seite 10529-10554 |
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| Übergeordnetes Werk: |
volume:112 ; year:2024 ; number:12 ; day:21 ; month:04 ; pages:10529-10554 |
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| DOI / URN: |
10.1007/s11071-024-09593-w |
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SPR05603654X |
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| 520 | |a Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. | ||
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| 650 | 4 | |a Medical image encryption |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Liu, Xiang |e verfasserin |4 aut | |
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10.1007/s11071-024-09593-w doi (DE-627)SPR05603654X (SPR)s11071-024-09593-w-e DE-627 ger DE-627 rakwb eng 510 VZ 30.20 bkl Ding, Dawei verfasserin aut Firing pattern transition of fractional-order memristor-coupled Hindmarsh–Rose neurons model and its medical image encryption for region of interest 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. Fractional-order memristor (dpeaa)DE-He213 HR neuron (dpeaa)DE-He213 Firing patterns (dpeaa)DE-He213 Medical image encryption (dpeaa)DE-He213 Region of interest (dpeaa)DE-He213 Chen, Siqi verfasserin aut Zhang, Hongwei verfasserin aut Yang, Zongli verfasserin aut Jin, Fan verfasserin aut Liu, Xiang verfasserin aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 112(2024), 12 vom: 21. Apr., Seite 10529-10554 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:112 year:2024 number:12 day:21 month:04 pages:10529-10554 https://dx.doi.org/10.1007/s11071-024-09593-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 VZ AR 112 2024 12 21 04 10529-10554 |
| spelling |
10.1007/s11071-024-09593-w doi (DE-627)SPR05603654X (SPR)s11071-024-09593-w-e DE-627 ger DE-627 rakwb eng 510 VZ 30.20 bkl Ding, Dawei verfasserin aut Firing pattern transition of fractional-order memristor-coupled Hindmarsh–Rose neurons model and its medical image encryption for region of interest 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. Fractional-order memristor (dpeaa)DE-He213 HR neuron (dpeaa)DE-He213 Firing patterns (dpeaa)DE-He213 Medical image encryption (dpeaa)DE-He213 Region of interest (dpeaa)DE-He213 Chen, Siqi verfasserin aut Zhang, Hongwei verfasserin aut Yang, Zongli verfasserin aut Jin, Fan verfasserin aut Liu, Xiang verfasserin aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 112(2024), 12 vom: 21. Apr., Seite 10529-10554 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:112 year:2024 number:12 day:21 month:04 pages:10529-10554 https://dx.doi.org/10.1007/s11071-024-09593-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 VZ AR 112 2024 12 21 04 10529-10554 |
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10.1007/s11071-024-09593-w doi (DE-627)SPR05603654X (SPR)s11071-024-09593-w-e DE-627 ger DE-627 rakwb eng 510 VZ 30.20 bkl Ding, Dawei verfasserin aut Firing pattern transition of fractional-order memristor-coupled Hindmarsh–Rose neurons model and its medical image encryption for region of interest 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. Fractional-order memristor (dpeaa)DE-He213 HR neuron (dpeaa)DE-He213 Firing patterns (dpeaa)DE-He213 Medical image encryption (dpeaa)DE-He213 Region of interest (dpeaa)DE-He213 Chen, Siqi verfasserin aut Zhang, Hongwei verfasserin aut Yang, Zongli verfasserin aut Jin, Fan verfasserin aut Liu, Xiang verfasserin aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 112(2024), 12 vom: 21. Apr., Seite 10529-10554 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:112 year:2024 number:12 day:21 month:04 pages:10529-10554 https://dx.doi.org/10.1007/s11071-024-09593-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 VZ AR 112 2024 12 21 04 10529-10554 |
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10.1007/s11071-024-09593-w doi (DE-627)SPR05603654X (SPR)s11071-024-09593-w-e DE-627 ger DE-627 rakwb eng 510 VZ 30.20 bkl Ding, Dawei verfasserin aut Firing pattern transition of fractional-order memristor-coupled Hindmarsh–Rose neurons model and its medical image encryption for region of interest 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. Fractional-order memristor (dpeaa)DE-He213 HR neuron (dpeaa)DE-He213 Firing patterns (dpeaa)DE-He213 Medical image encryption (dpeaa)DE-He213 Region of interest (dpeaa)DE-He213 Chen, Siqi verfasserin aut Zhang, Hongwei verfasserin aut Yang, Zongli verfasserin aut Jin, Fan verfasserin aut Liu, Xiang verfasserin aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 112(2024), 12 vom: 21. Apr., Seite 10529-10554 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:112 year:2024 number:12 day:21 month:04 pages:10529-10554 https://dx.doi.org/10.1007/s11071-024-09593-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 VZ AR 112 2024 12 21 04 10529-10554 |
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10.1007/s11071-024-09593-w doi (DE-627)SPR05603654X (SPR)s11071-024-09593-w-e DE-627 ger DE-627 rakwb eng 510 VZ 30.20 bkl Ding, Dawei verfasserin aut Firing pattern transition of fractional-order memristor-coupled Hindmarsh–Rose neurons model and its medical image encryption for region of interest 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. Fractional-order memristor (dpeaa)DE-He213 HR neuron (dpeaa)DE-He213 Firing patterns (dpeaa)DE-He213 Medical image encryption (dpeaa)DE-He213 Region of interest (dpeaa)DE-He213 Chen, Siqi verfasserin aut Zhang, Hongwei verfasserin aut Yang, Zongli verfasserin aut Jin, Fan verfasserin aut Liu, Xiang verfasserin aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 112(2024), 12 vom: 21. Apr., Seite 10529-10554 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:112 year:2024 number:12 day:21 month:04 pages:10529-10554 https://dx.doi.org/10.1007/s11071-024-09593-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 VZ AR 112 2024 12 21 04 10529-10554 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. 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Firing pattern transition of fractional-order memristor-coupled Hindmarsh–Rose neurons model and its medical image encryption for region of interest |
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Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Firing pattern transition of fractional-order memristor-coupled Hindmarsh–Rose neurons model and its medical image encryption for region of interest |
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https://dx.doi.org/10.1007/s11071-024-09593-w |
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Chen, Siqi Zhang, Hongwei Yang, Zongli Jin, Fan Liu, Xiang |
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Chen, Siqi Zhang, Hongwei Yang, Zongli Jin, Fan Liu, Xiang |
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10.1007/s11071-024-09593-w |
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2024-07-03T19:45:31.649Z |
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| score |
7.4014044 |