An Efficient Spiking Neuron Hardware System Based on the Hardware-Oriented Modified Izhikevich Neuron (HOMIN) Model

Mathematical modifications have been made to the Izhikevich Neuron Model to allow for a simple, low-area digital hardware implementation with low computational intensity. The implemented neuron circuit only requires one input parameter to replicate all of the cortical neuron behaviours described by Izhikevich. The low hardware requirements of this neuron implementation make large, highly parallel digital spiking neural networks of this neuron feasible. Additionally, the model requires fewer external parameter changes to exhibit diverse neuron behaviours. The alterations made to create this novel model are presented in detail with a performance comparison to the original Izhikevich Neuron. Subsequently a digital hardware realization is presented and its performance is characterized.

对伊齐基维奇神经元模型进行了数学修改，以实现一种简单、面积小且计算强度低的数字硬件实现方式。所实现的神经元电路仅需一个输入参数，即可重现伊齐基维奇所描述的所有皮质神经元行为。这种神经元实现方式对硬件要求较低，使得构建大规模、高度并行的这种神经元数字脉冲神经网络成为可能。此外，该模型只需较少的外部参数变化就能呈现出多种神经元行为。详细介绍了为创建这个新模型所做的改动，并与原始的伊齐基维奇神经元进行了性能比较。随后介绍了一种数字硬件实现方式，并对其性能进行了描述。