This　paper　focuses　on　the　stochastic　passivity　problem　of　stochastic　memristor-based　complex　valued　neural　networks　with　two　different　types　of　time-delays　and　reaction-diffusion　terms　by　sampled-data　control　strategy.　Different　from　the　existing　sampled-data　strategies,　this　paper　develops　spatial　and　temporal　point　sampling,　namely,　only　a　finite　number　of　points　in　space　or　time　are　sampled.　By　introducing　two　different　Lyapunov　functional　and　employing　techniques　such　as　Wirtinger＇s　integral　inequality,　Jensen＇s　inequality　and　Young＇s　inequality,　etc.,　two　different　sufficient　conditions　for　the　stochastic　passivity　of　the　system　are　established.　Prominently,　the　condition　quantitatively　reveals　the　relationship　between　the　upper　and　lower　bounds　of　the　sampling　interval　at　spatial　and　temporal　points.　Finally,　a　numerical　example　is　given　to　verify　the　rationality　of　the　proposed　method.　Notice,　compared　with　a　large　number　of　results　of　real-valued　reaction-diffusion　neural　networks,　the　research　results　of　sampled-data　controlled　complex-valued　reaction-diffusion　neural　networks　have　not　appeared　so　far,　and　this　work　is　the　first　attempt　to　fill　in　the　gaps　in　this　topic.　(c)　2022　The　Franklin　Institute.　Published　by　Elsevier　Ltd.　All　rights　reserved.