We study the effects of different bio-synaptic membrane potential mechanisms on the inference speed of both spiking feed-forward neural networks and spiking convolutional neural networks. These mechanisms are inspired by biological neuron phenomena include electronic conduction in neurons and chemical neurotransmitter attenuation between presynaptic and postsynaptic neurons. In the area of spiking neural networks, we model some biological neural membrane potential updating strategies based on integrate-and-fire (I&F) spiking neurons. These include the spiking neuron model with membrane potential decay (MemDec), the spiking neuron model with synaptic input current superposition at spiking time (SynSup), and the spiking neuron model with synaptic input current accumulation (SynAcc). Experiment results show that compared with the general I&F model (one of the most commonly used spiking neuron models), SynSup and SynAcc can effectively improve the spiking inference speed of spiking feed-forward neural networks and spiking convolutional neural networks.

我们研究了不同的生物突触膜电位机制对尖峰前馈神经网络和尖峰卷积神经网络的推理速度的影响。这些机制受到生物神经元现象的启发，包括神经元中的电子传导以及突触前和突触后神经元之间的化学神经递质衰减。在尖峰神经网络领域，我们对基于积分并发射（I＆F）尖峰神经元的一些生物神经膜电位更新策略进行建模。这些包括具有膜电位衰减的尖峰神经元模型（MemDec），带有尖峰时间的突触输入电流叠加的尖峰神经元模型（SynSup）和带有突触输入电流累积的尖峰神经元模型（SynAcc）。实验结果表明，与一般I＆