Low power consuming biomimetic neurons are considered for use in analyzing electrophysiological data. Starting with a circuit model of a Morris-Lecar inspired spiking neuron, we first investigate the dynamic properties. We demonstrate some of its neuro-computational features including type I and type II excitability, tonic and phasic spiking, spike latency and integration. Electroencephalogram (EEG) signals are then used as excitatory input currents and it is shown that the spiking neurons can provide new insights into brain function. The spike rates of the neurons are employed in a classification task and shown to yield similar performance compared to one using the frequency dependence. We discuss how this circuit has the potential to significantly reduce EEG data, improve privacy and lower power consumption for portable EEG systems.

中文翻译：

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刺入感觉神经元以分析电生理数据

低功耗仿生神经元被认为可用于分析电生理数据。从Morris-Lecar激发的尖峰神经元的电路模型开始，我们首先研究动态特性。我们展示了它的一些神经计算功能，包括I型和II型兴奋性，强直性和相性尖峰，尖峰潜伏期和整合。然后将脑电图（EEG）信号用作兴奋性输入电流，并显示出尖峰神经元可以提供对脑功能的新见解。神经元的尖峰率用于分类任务，与使用频率依赖性的神经元尖峰率相比，显示出相似的性能。我们讨论了该电路如何具有潜力，可大大减少EEG数据，改善隐私性并降低便携式EEG系统的功耗。