Spiking Neural Networks (SNNs) have piqued researchers' interest because of their capacity to process temporal information and low power consumption. However, current state-of-the-art methods limited their biological plausibility and performance because their neurons are generally built on the simple Leaky-Integrate-and-Fire (LIF) model. Due to the high level of dynamic complexity, modern neuron models have seldom been implemented in SNN practice. In this study, we adopt the Phase Plane Analysis (PPA) technique, a technique often utilized in neurodynamics field, to integrate a recent neuron model, namely, the Izhikevich neuron. Based on the findings in the advancement of neuroscience, the Izhikevich neuron model can be biologically plausible while maintaining comparable computational cost with LIF neurons. By utilizing the adopted PPA, we have accomplished putting neurons built with the modified Izhikevich model into SNN practice, dubbed as the Standardized Izhikevich Tonic (SIT) neuron. For performance, we evaluate the suggested technique for image classification tasks in self-built LIF-and-SIT-consisted SNNs, named Hybrid Neural Network (HNN) on static MNIST, Fashion-MNIST, CIFAR-10 datasets and neuromorphic N-MNIST, CIFAR10-DVS, and DVS128 Gesture datasets. The experimental results indicate that the suggested method achieves comparable accuracy while exhibiting more biologically realistic behaviors on nearly all test datasets, demonstrating the efficiency of this novel strategy in bridging the gap between neurodynamics and SNN practice.
翻译:Spik Neal Networks(SNN) 128 研究人员的兴趣来自他们处理时间信息的能力和低电能消耗的能力,然而,目前最先进的方法限制了他们的生物光度和性能,因为其神经元一般建在简单的Leaky-Integrate-and-Fire(LIF)模型上。由于高度的动态复杂性,现代神经模型很少在SNN实践中实施。在这项研究中,我们采用了SNNF(PPA)阶段的精度分析(PPA)技术,这是神经动力学领域经常使用的一种技术,以整合最新的神经模型,即Izhikevich神经元。基于神经科学进步方面的调查结果,Izhikevich神经模型在生物科学进步方面可能具有生物光亮度,同时保持与LIF神经的可比计算成本。我们通过采用PPA(PPA)完成了将经修改的Izhikevich 模型建造神经元的神经元纳入SNNF(S-NF-NF-NF) 战略中,我们评估了所有标准化 Izikevevevevle-NIS-NIS-IL(S 和S 的S-IF-NIS-IG-IG-IG-IL(S) 的S 方法,同时在S-ID-ID-ID-ID-ID-ID-ID-ID-ID-ID-IG-IG-S-ID-ID-ID-S-S-S-ID-ID-ID-ID-S-S-S-S-ID-S-S-S-S-S-S-S-S-S-S-S-ID-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-ID-S-S-S-S-S-S-S-S-