The　propagation　of　subthreshold　signals　is　an　important　part　of　information　exchange　in　neural　systems,　but　the　propagation　mechanism　of　subthreshold　signals　in　neural　networks　and　the　influence　of　internal　and　external　factors　on　propagation　are　still　unclear.　Based　on　this,　firstly,　a　mathematical　model　for　the　transmission　of　subthreshold　excitatory　postsynaptic　current　(EPSC)　signals　in　a　multi-layer　feedforward　neural　network　is　constructed.　Each　element　is　a　HH　neuron　model.　To　simulate　the　connections　between　neurons　in　different　layers　of　the　cerebral　cortex　more　closely,　a　multi-factor　WS　small-world　network　(MF-WS)　is　proposed,　which　can　form　inter-layer　differences　by　adjusting　multiple　factors.　Then,　according　to　the　influence　factors　of　MF-WS　small-world　network　formation,　explore　its　influence　on　EPSC　signal　transmission.　Through　research,　the　increase　of　synaptic　coupling　strength　W,　the　number　of　neighbors　K　and　the　proportion　of　excitatory　neurons　R　are　conducive　to　the　propagation　of　EPSC　signals.　With　the　increase　of　the　respective　weights　of　multiple　factors,　the　optimal　noise　intensity　conducive　to　the　propagation　of　EPSC　signals　decreases.　Finally,　a　multi-factor-consistent　BP　neural　network　(MF-C-BP)　is　proposed　to　optimize　the　synaptic　coupling　strength　and　connection　mode　between　layers,　which　increases　the　consistency　of　the　neural　network　pulse　discharge　sequence　after　the　EPSC　signal　transmission　to　real　correlation　(±　0.30　-　±　0.50)　or　significant　correlation　(±　0.50　-　±　0.80).　By　studying　the　propagation　mechanism　of　EPSC　signal　and　the　consistency　of　pulse　discharge,　a　more　potential　mechanism　for　EPSC　signal　coding　in　neural　network　is　provided.　©　2024　SPIE