Neuromorphic　computation,　which　emulates　the　signal　process　of　the　human　brain,　is　considered　to　be　a　feasible　way　for　future　computation.　Realization　of　dynamic　modulation　of　synaptic　plasticity　and　accelerated　learning,　which　could　improve　the　processing　capacity　and　learning　ability　of　artificial　synaptic　devices,　is　considered　to　further　improve　energy　efficiency　of　neuromorphic　computation.　Nevertheless,　realization　of　dynamic　regulation　of　synaptic　weight　without　an　external　regular　terminal　and　the　method　that　could　endow　artificial　synaptic　devices　with　the　ability　to　modulate　learning　speed　have　rarely　been　reported.　Furthermore,　finding　suitable　materials　to　fully　mimic　the　response　of　photoelectric　stimulation　is　still　challenging　for　photoelectric　synapses.　Here,　a　floating　gate　synaptic　transistor　based　on　an　L-type　ligand-modified　all-inorganic　CsPbBr3　perovskite　quantum　dots　is　demonstrated.　This　work　provides　first　clear　experimental　evidence　that　the　synaptic　plasticity　can　be　dynamically　regulated　by　changing　the　waveforms　of　action　potential　and　the　environment　temperature　and　both　of　these　parameters　originate　from　and　are　crucial　in　higher　organisms.　Moreover,　benefiting　from　the　excellent　photoelectric　properties　and　stability　of　quantum　dots,　a　temperature-facilitated　learning　process　is　illustrated　by　the　classical　conditioning　experiment　with　and　without　illumination,　and　the　mechanism　of　synaptic　plasticity　is　also　demonstrated.　This　work　offers　a　feasible　way　to　realize　dynamic　modulation　of　synaptic　weight　and　accelerating　the　learning　process　of　artificial　synapses,　which　showed　great　potential　in　the　reduction　of　energy　consumption　and　improvement　of　efficiency　of　future　neuromorphic　computing.