Objective:　Lobula　giant　motion　detectors　(LGMDs)　in　locusts　effectively　predict　collisions　and　trigger　avoidance,　with　potential　applications　in　autonomous　driving　and　UAVs.　Research　on　LGMD　characteristics　splits　into　two　views:　one　focusing　on　the　presynaptic　visual　pathway,　the　other　on　the　postsynaptic　LGMD　neurons.　Both　perspectives　have　support,　leading　to　two　computational　models,　but　they　lack　a　biophysical　description　of　the　individual　LGMD　neuron　behavior.　This　paper　aims　to　mimic　and　explain　LGMD　behavior　based　on　fractional　spiking　neurons　(FSNs)　and　construct　a　biomimetic　visual　model　for　the　LGMD　compatible　with　these　characteristics.　Methods:　We　implement　the　visual　model　using　an　event　camera　to　simulate　photoreceptors　and　follow　the　ON/OFF　visual　pathway,　incorporating　lateral　inhibition　to　mimic　the　LGMD　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　introduce　FSN　circuits　by　altering　dendritic　morphological　parameters　to　simulate　multi-scale　spike　frequency　adaptation　(SFA)　observed　in　LGMDs.　Additionally,　we　add　one　more　circuit　of　dendritic　trees　into　the　FSNs　to　be　compatible　with　the　postsynaptic　feed-forward　inhibition　(FFI)　in　LGMD　neurons,　providing　a　novel　explanatory　and　predictive　model.　Results:　We　test　that　the　event-driven　biomimetic　visual　model　can　achieve　collision　detection　and　looming　selection　in　different　complex　scenes,　especially　fast-moving　objects.