Firing mechanism based on single memristive neuron and double memristive coupled neurons
Abstract Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shen, Hui [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor 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, 110(2022), 4 vom: 27. Aug., Seite 3807-3822 |
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| Übergeordnetes Werk: |
volume:110 ; year:2022 ; number:4 ; day:27 ; month:08 ; pages:3807-3822 |
| Links: |
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| DOI / URN: |
10.1007/s11071-022-07812-w |
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OLC2080095048 |
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| 520 | |a Abstract Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. | ||
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10.1007/s11071-022-07812-w doi (DE-627)OLC2080095048 (DE-He213)s11071-022-07812-w-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Shen, Hui verfasserin aut Firing mechanism based on single memristive neuron and double memristive coupled neurons 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor 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 Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. Memristor Fitzhugh–Nagumo (FN) neuron Hindmarsh–Rose (HR) neuron Coupling Firing FPGA Yu, Fei (orcid)0000-0002-3091-7640 aut Wang, Chunhua aut Sun, Jingru aut Cai, Shuo (orcid)0000-0003-4375-3187 aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 110(2022), 4 vom: 27. Aug., Seite 3807-3822 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:110 year:2022 number:4 day:27 month:08 pages:3807-3822 https://doi.org/10.1007/s11071-022-07812-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT AR 110 2022 4 27 08 3807-3822 |
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10.1007/s11071-022-07812-w doi (DE-627)OLC2080095048 (DE-He213)s11071-022-07812-w-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Shen, Hui verfasserin aut Firing mechanism based on single memristive neuron and double memristive coupled neurons 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor 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 Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. Memristor Fitzhugh–Nagumo (FN) neuron Hindmarsh–Rose (HR) neuron Coupling Firing FPGA Yu, Fei (orcid)0000-0002-3091-7640 aut Wang, Chunhua aut Sun, Jingru aut Cai, Shuo (orcid)0000-0003-4375-3187 aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 110(2022), 4 vom: 27. Aug., Seite 3807-3822 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:110 year:2022 number:4 day:27 month:08 pages:3807-3822 https://doi.org/10.1007/s11071-022-07812-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT AR 110 2022 4 27 08 3807-3822 |
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10.1007/s11071-022-07812-w doi (DE-627)OLC2080095048 (DE-He213)s11071-022-07812-w-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Shen, Hui verfasserin aut Firing mechanism based on single memristive neuron and double memristive coupled neurons 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor 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 Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. Memristor Fitzhugh–Nagumo (FN) neuron Hindmarsh–Rose (HR) neuron Coupling Firing FPGA Yu, Fei (orcid)0000-0002-3091-7640 aut Wang, Chunhua aut Sun, Jingru aut Cai, Shuo (orcid)0000-0003-4375-3187 aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 110(2022), 4 vom: 27. Aug., Seite 3807-3822 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:110 year:2022 number:4 day:27 month:08 pages:3807-3822 https://doi.org/10.1007/s11071-022-07812-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT AR 110 2022 4 27 08 3807-3822 |
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10.1007/s11071-022-07812-w doi (DE-627)OLC2080095048 (DE-He213)s11071-022-07812-w-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Shen, Hui verfasserin aut Firing mechanism based on single memristive neuron and double memristive coupled neurons 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor 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 Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. Memristor Fitzhugh–Nagumo (FN) neuron Hindmarsh–Rose (HR) neuron Coupling Firing FPGA Yu, Fei (orcid)0000-0002-3091-7640 aut Wang, Chunhua aut Sun, Jingru aut Cai, Shuo (orcid)0000-0003-4375-3187 aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 110(2022), 4 vom: 27. Aug., Seite 3807-3822 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:110 year:2022 number:4 day:27 month:08 pages:3807-3822 https://doi.org/10.1007/s11071-022-07812-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT AR 110 2022 4 27 08 3807-3822 |
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10.1007/s11071-022-07812-w doi (DE-627)OLC2080095048 (DE-He213)s11071-022-07812-w-p DE-627 ger DE-627 rakwb eng 510 VZ 11 ssgn Shen, Hui verfasserin aut Firing mechanism based on single memristive neuron and double memristive coupled neurons 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor 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 Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. Memristor Fitzhugh–Nagumo (FN) neuron Hindmarsh–Rose (HR) neuron Coupling Firing FPGA Yu, Fei (orcid)0000-0002-3091-7640 aut Wang, Chunhua aut Sun, Jingru aut Cai, Shuo (orcid)0000-0003-4375-3187 aut Enthalten in Nonlinear dynamics Springer Netherlands, 1990 110(2022), 4 vom: 27. Aug., Seite 3807-3822 (DE-627)130936782 (DE-600)1058624-6 (DE-576)034188126 0924-090X nnns volume:110 year:2022 number:4 day:27 month:08 pages:3807-3822 https://doi.org/10.1007/s11071-022-07812-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT AR 110 2022 4 27 08 3807-3822 |
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firing mechanism based on single memristive neuron and double memristive coupled neurons |
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Firing mechanism based on single memristive neuron and double memristive coupled neurons |
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Abstract Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor 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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Abstract Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor 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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Abstract Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor 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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