Serial　section　electron　microscopy　(ssEM)　can　provide　comprehensive　3D　ultrastructural　information　of　the　brain　with　exceptional　computational　cost.　Targeted　reconstruction　of　subcellular　structures　from　ssEM　da-tasets　is　less　computationally　demanding　but　still　highly　informative.　We　thus　developed　a　region-CNN　-based　deep　learning　method　to　identify,　segment,　and　reconstruct　synapses　and　mitochondria　to　explore　the　structural　plasticity　of　synapses　and　mitochondria　in　the　auditory　cortex　of　mice　subjected　to　fear　con-ditioning.　Upon　reconstructing　over　135,000　mitochondria　and　160,000　synapses,　we　find　that　fear　condition-ing　significantly　increases　the　number　of　mitochondria　but　decreases　their　size　and　promotes　formation　of　multi-contact　synapses,　comprising　a　single　axonal　bouton　and　multiple　postsynaptic　sites　from　different　dendrites.　Modeling　indicates　that　such　multi-contact　configuration　increases　the　information　storage　ca-pacity　of　new　synapses　by　over　50%.　With　high　accuracy　and　speed　in　reconstruction,　our　method　yields　structural　and　functional　insight　into　cellular　plasticity　associated　with　fear　learning.