  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.
