Objective:　As　biological　wide-field　visual　neurons　in　locusts,　lobula　giant　motion　detectors　(LGMDs)　can　effectively　predict　collisions　and　trigger　avoidance　before　the　collision　occurs.　This　capability　has　extensive　potential　applications　in　autonomous　driving,　unmanned　aerial　vehicles,　and　more.　Currently,　describing　the　LGMD　characteristics　is　divided　into　two　viewpoints,　one　emphasizing　the　presynaptic　visual　pathway　and　the　other　emphasizing　the　postsynaptic　LGMDs　neuron.　Indeed,　both　have　their　research　support　leading　to　the　emergence　of　two　computational　models,　but　both　lack　a　biophysical　description　of　the　behavior　in　the　individual　LGMD　neuron.　This　paper　aims　to　mimic　and　explain　LGMD＇s　behavior　based　on　fractional　spiking　neurons　and　construct　a　biomimetic　visual　model　for　the　LGMD　compatible　with　these　two　characteristics.　Methods:　We　implement　the　visual　model　in　the　form　of　spikes　by　choosing　an　event　camera　rather　than　a　conventional　CMOS　camera　to　simulate　the　photoreceptors　and　follow　the　topology　of　the　ON/OFF　visual　pathway,　enabling　it　to　incorporate　the　lateral　inhibition　to　mimic　the　LGMD＇s　system　from　the　bottom　up.　Second,　most　computational　models　of　motion　perception　use　only　the　dendrites　within　the　LGMD　neurons　as　the　ideal　pathway　for　linear　summation,　ignoring　dendritic　effects　inducing　neuronal　properties.　Thus,　we　introduced　fractional　spiking　neuron　(FSN)　circuits　into　the　model　by　altering　dendritic　morphological　parameters　to　simulate　multi-scale　spike　frequency　adaptation　(SFA)　observed　in　LGMDs.　In　addition,　we　have　attempted　to　add　one　more　circuit　of　dendritic　trees　into　fractional　spiking　neurons　to　be　compatible　with　the　postsynaptic　FFI　in　LGMDs　and　provide　a　novel　explanatory　approach　and　a　predictive　model　for　studying　LGMD　neurons.　Results:　Finally,　we　test　that　the　event-driven　biomimetic　visual　model　can　achieve　collision　detection　and　looming　selection　in　different　complex　scenes,　especially　fast-moving　objects.　IEEE