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.