Neuron　reconstruction　algorithms　used　in　electron　microscope　volumes　have　received　increasing　attention　in　recent　years.　Most　current　methods　are　highly　reliant　on　neuron　membrane　boundary　evidence　without　considering　biological　plausibility.　In　this　investigation,　we　present　a　novel　neuron　reconstruction　framework　via　the　fusion　of　a　global　optimization　goal　and　biologically　inspired　priors.　We　encode　the　3D　instances　of　synapses　and　mitochondria　as　two　types　of　constraints　to　allow　for　the　direct　inclusion　of　non-local　connectivity　information　in　the　neuron　segmentation.　Moreover,　a　flexible　decision　procedure　is　designed　to　retain　high　-confidence　priors　to　deal　with　the　possible　influence　of　the　upstream　ultrastructure　error.　We　construct　the　constrained　graph　partitioning　model　and　adapt　two　greedy　algorithms　with　the　polynomial　time　complexity　to　solve　the　proposed　model.　We　perform　comparative　studies　on　several　public　datasets　and　demonstrate　that　the　decision　of　ultrastructural　connectivity　constraints　contributes　to　significant　improvements　over　existing　hierarchical　agglomeration　algorithms.　The　ablation　studies　of　ultrastructures　from　different　recognition　accuracy　suggest　the　generality　and　applicability　of　the　proposed　method.