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0.65 V integrable electronic realisation of integer- and fractional-order Hindmarsh–Rose neuron model using companding technique

机译:使用压扩技术对整数和分数阶Hindmarsh–Rose神经元模型进行0.65 V可积电子实现

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摘要

Some neurons like neocortical pyramidal neurons adapt with multiple time-scales, which is consistent with fractional-order differentiation. The fractional-order neuron models are therefore believed to portray the firing rate of neurons more accurately than their integer-order models. It has been studied that as the fractional order of differentiator and integrator involved in the neuron model decreases, bursting frequency of the neurons increases. The opposite effect has been observed on increasing the external excitation. In this study, integer- and fractional-order Hindmarsh-Rose (HR) neuron models have been implemented using sinh companding technique. Besides, the application of the HR neuron model in a simple network of two neurons has also been considered. The designs offer a low-voltage and low-power implementation along with the electronic tunability of the performance characteristics. Due to the use of only metal-oxide semiconductor (MOS) transistors and grounded capacitors, the proposed implementation can be integrated in chip form. On comparing with existing implementations, the implemented fractional-order and integer-order models show a better performance in terms of power consumption, supply voltage, order and flexibility. The performance of the circuits has been verified using 130 nm complementary MOS (CMOS) technology process provided by Austrian Micro Systems using HSPICE simulation software.
机译:一些神经元(如新皮质锥体神经元)会适应多个时间尺度,这与分数阶分化是一致的。因此,认为分数阶神经元模型比其整数阶模型更准确地描绘了神经元的放电速率。已经研究出,随着神经元模型中所涉及的微分和积分的分数阶减小,神经元的爆发频率增加。在增加外部激励方面观察到相反的效果。在这项研究中,整数和分数阶Hindmarsh-Rose(HR)神经元模型已使用sinh压扩技术实现。此外,还考虑了HR神经元模型在两个神经元的简单网络中的应用。这些设计提供了低电压和低功耗的实现方式以及性能特征的电子可调性。由于仅使用金属氧化物半导体(MOS)晶体管和接地电容器,因此可以以芯片形式集成建议的实现。与现有的实现方式相比,已实现的分数阶和整数阶模型在功耗,电源电压,阶数和灵活性方面表现出更好的性能。电路的性能已通过奥地利微系统公司使用HSPICE仿真软件提供的130 nm互补MOS(CMOS)工艺进行了验证。

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