Currently,　most　state-of-the-art　pipelines　for　3D　micro-connectomic　reconstruction　deal　with　neuron　over-segmentation,　agglomeration　and　subcellular　compartment　(nuclei,　mitochondria,　synapses,　etc.)　detection　separately.　Inspired　by　the　proofreading　consensus　of　experts,　we　established　a　paradigm　to　acquire　priori　knowledge　of　cellular　characteristics　and　ultrastructures,　as　well　as　determine　the　connectivity　of　neural　circuits　simultaneously.　Following　this　novel　paradigm,　we　were　keen　to　bring　the　Intra-　and　Inter-Cellular　Awareness　back　when　Tracking　and　Segmenting　neurons　in　connectomics.　Our　proposed　method　(II-CATS)　utilizes　few-shot　learning　techniques　to　encode　the　internal　neurite　representation　and　its　learnable　components,　which　could　significantly　impact　neuron　tracings.　We　further　go　beyond　the　original　expected　run　length　(ERL)　metric　by　focusing　on　biological　constraints　(bERL)　or　spanning　from　the　nucleus　to　spines　(nERL).　With　the　evaluation　of　these　metrics,　we　perform　typical　experiments　on　multiple　electron　microscopy　datasets　on　diverse　animals　and　scales.　In　particular,　our　proposed　method　is　naturally　suitable　for　tracking　neurons　that　have　been　identified　by　staining.