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.