Dynamics and stochastic resonance in a thermosensitive neuron
•A functional neural circuit is proposed by incorporating thermistor.•A thermosensitive neuron is obtained to percept temperature.•Rich bifurcation dynamics and energy dependence on mode are presented. Temperature has distinct impact on the activation of neural activities by adjusting the excitabili...
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| Vydáno v: | Applied mathematics and computation Ročník 385; s. 125427 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
15.11.2020
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| Témata: | |
| ISSN: | 0096-3003 |
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| Abstract | •A functional neural circuit is proposed by incorporating thermistor.•A thermosensitive neuron is obtained to percept temperature.•Rich bifurcation dynamics and energy dependence on mode are presented.
Temperature has distinct impact on the activation of neural activities by adjusting the excitability and channel conductance. Some biological neurons can percept slight changes of temperature and then appropriate firing modes are induced under different temperatures. Indeed, the temperature-dependent property can be described by using thermistors, which can be included to build functional neural circuits. Therefore, any changes in the resistance of the thermistor can regulate the branch current and also the output voltage completely. In the paper, thermistors are connected to the FitzHugh-Nagumo neural circuit driven by a voltage source, and a thermosensitive neuron oscillator is obtained by applying scale transformation for the physical variables and parameters in the neural circuit. The thermistor is connected to different branch circuits to enhance the sensitivity to changes of temperature. It is confirmed that the neural activities can present distinct mode transition from spiking to bursting and chaotic states. In particular, the external stimulus becomes dependent on temperature when the thermistor is connected to the external voltage source. Stochastic resonance and multiple modes are detected when additive Gaussian white noise is applied on an isolated neuron controlled by temperature. Therefore, a sensitive sensor of temperature is obtained, and this feasible neuron model can be effective to investigate the collective behaviors of neural networks in the presence of time-varying temperature. Also, the Hamilton energy is estimated to predict the mode selection and effect of noise. |
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| AbstractList | •A functional neural circuit is proposed by incorporating thermistor.•A thermosensitive neuron is obtained to percept temperature.•Rich bifurcation dynamics and energy dependence on mode are presented.
Temperature has distinct impact on the activation of neural activities by adjusting the excitability and channel conductance. Some biological neurons can percept slight changes of temperature and then appropriate firing modes are induced under different temperatures. Indeed, the temperature-dependent property can be described by using thermistors, which can be included to build functional neural circuits. Therefore, any changes in the resistance of the thermistor can regulate the branch current and also the output voltage completely. In the paper, thermistors are connected to the FitzHugh-Nagumo neural circuit driven by a voltage source, and a thermosensitive neuron oscillator is obtained by applying scale transformation for the physical variables and parameters in the neural circuit. The thermistor is connected to different branch circuits to enhance the sensitivity to changes of temperature. It is confirmed that the neural activities can present distinct mode transition from spiking to bursting and chaotic states. In particular, the external stimulus becomes dependent on temperature when the thermistor is connected to the external voltage source. Stochastic resonance and multiple modes are detected when additive Gaussian white noise is applied on an isolated neuron controlled by temperature. Therefore, a sensitive sensor of temperature is obtained, and this feasible neuron model can be effective to investigate the collective behaviors of neural networks in the presence of time-varying temperature. Also, the Hamilton energy is estimated to predict the mode selection and effect of noise. |
| ArticleNumber | 125427 |
| Author | Xu, Ying Ma, Jun Ren, Guodong Guo, Yeye |
| Author_xml | – sequence: 1 givenname: Ying surname: Xu fullname: Xu, Ying organization: Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China – sequence: 2 givenname: Yeye surname: Guo fullname: Guo, Yeye organization: Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China – sequence: 3 givenname: Guodong surname: Ren fullname: Ren, Guodong organization: Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China – sequence: 4 givenname: Jun surname: Ma fullname: Ma, Jun email: hyperchaos@163.com, hyperchaos@lut.edu.cn organization: Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China |
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