PDF(Pubmed)
Abstract：
The functional neurons are basic building blocks of the nervous system and are responsible for transmitting information between different parts of the body. However, it is less known about the interaction between the neuron and the field. In this work, we propose a novel functional neuron by introducing a flux-controlled memristor into the FitzHugh-Nagumo neuron model, and the field effect is estimated by the memristor. We investigate the dynamics and energy characteristics of the neuron, and the stochastic resonance is also considered by applying the additive Gaussian noise. The intrinsic energy of the neuron is enlarged after introducing the memristor. Moreover, the energy of the periodic oscillation is larger than that of the adjacent chaotic oscillation with the changing of memristor-related parameters, and same results is obtained by varying stimuli-related parameters. In addition, the energy is proved to be another effective method to estimate stochastic resonance and inverse stochastic resonance. Furthermore, the analog implementation is achieved for the physical realization of the neuron. These results shed lights on the understanding of the firing mechanism for neurons detecting electromagnetic field.
摘要：
功能性神经元是神经系统的基本组成部分，负责在身体不同部位之间传递信息。然而,关于神经元和磁场之间的相互作用知之甚少。在这项工作中,我们通过在FitzHugh-Nagumo神经元模型中引入通量控制的忆阻器，提出了一种新的功能神经元，场效应由忆阻器估计。我们研究了神经元的动力学和能量特征，并通过应用加性高斯噪声来考虑随机共振。在引入忆阻器之后,神经元的固有能量被放大。此外，随着忆阻器相关参数的变化，周期性振荡的能量大于相邻混沌振荡的能量，通过改变刺激相关参数可以获得相同的结果。此外,能量被证明是估计随机共振和逆随机共振的另一种有效方法。此外，模拟实现是为神经元的物理实现而实现的。这些结果为了解神经元检测电磁场的激发机制提供了启示。
