Recent　advances　in　resistive　synaptic　devices　have　enabled　the　emergence　of　brain-inspired　smart　chips.　These　chips　can　execute　complex　cognitive　tasks　in　digital　signal　processing　precisely　and　efficiently　using　an　efficient　neuromorphic　system.　The　neuromorphic　synapses　used　in　such　chips,　however,　are　very　sensitive　to　the　external　environment,　thereby　weakening　their　resistance　to　malicious　modifications　such　as　hardware　Trojans　and　backdoors.　Accordingly,　in　this　paper,　we　propose　HTcatcher,　a　security　verification　technique　for　hardware　threat　detection　in　neuromorphic　computing　systems,　incorporating　finite　state　machine　and　feature　verification　simultaneously,　which　has　never　been　considered　in　prior　work.　Furthermore,　we　propose　a　pseudo-random　matrix　verifying　technique　for　memory　optimization,　which　can　reduce　the　memory　overhead　of　the　multi-dimensional　features　in　the　system　significantly.　Experimental　results　confirm　that　the　proposed　method　can　identify　the　malicious　modifications　in　the　system　accurately,　while　reducing　the　memory　usage　by　25%-50%.　©　2020　Association　for　Computing　Machinery.