The　neuron　model　serves　as　the　foundation　for　building　a　neural　network.　The　goal　of　neuron　modeling　is　to　shoot　a　tradeoff　between　the　biological　meaningful　and　the　implementation　cost,　so　as　to　build　a　bridge　between　brain　science　knowledge　and　the　brain-like　neuromorphic　computing.　Unlike　previous　neuron　models　with　linear　static　synapses,　the　focus　of　this　research　is　to　model　neurons　with　relatively　detailed　nonlinear　dynamic　synapses.　First,　a　universal　soma-synapses　neuron　(SSN)　is　proposed.　It　contains　a　soma　represented　by　a　leaky　integrate-and-fire　neuron　and　multiple　excitatory　and　inhibitory　synapses　based　on　ion　channels　dynamics.　Short-term　plasticity　and　spike-timing-dependent　plasticity　linked　to　biological　microscopic　mechanisms　are　also　presented　in　the　synaptic　models.　Then,　SSN　is　implemented　on　field-programmable　gate　array　(FPGA).　The　performance　of　each　component　in　SSN　is　analyzed　and　evaluated.　Finally,　a　neural　network　SSNN　composed　of　SSNs　is　deployed　on　FPGA　and　used　for　testing.　Experimental　results　show　that　the　stimulus-response　characteristics　of　SSN　are　consistent　with　the　electrophysiological　test　findings　of　biological　neurons,　and　the　activities　of　SSNN　exhibit　a　promising　prospect.　We　provide　a　prototype　for　embedded　neuromorphic　computing　with　a　small　number　of　relatively　detailed　neuron　models.