Nanomaterial-based　memristors　with　analog　resistive　switching　properties　are　used　in　the　study　of　electronic　synapses,　providing　information　on　both　nanoscale　device　physics　and　low-power　neuromorphic　computing　applications.　Here,　a　memristor　based　on　individual　ZnO　nanowires　is　prepared　to　study　synaptic　learning　rules.　Hebbian　plasticity　modulation　is　achieved　with　the　co-application　of　pre-　and　post-synaptic　spikes　by　tuning　the　temporal　difference,　spike　frequency　and　voltage　amplitude.　Additionally,　synaptic　saturation　is　observed　to　stabilize　the　growth　of　synaptic　weights.　Plasma　treatment　of　the　memristors　was　performed　to　investigate　its　effects　on　synaptic　plasticity　and　conductance　modulation　linearity　during　resistive　switching.　Plasma　treatment　allowed　gradual　conductance　modulation　of　the　memristor　to　be　obtained,　with　improved　conductance　modulation　linearity,　suggesting　that　the　memristor　is　capable　of　implementing　synaptic　plasticity　to　serve　learning　and　memory.　It　was　observed　that　the　plasma　treatment　could　also　extend　synaptic　weight　changes　(?w)　to　enhance　learning　capability　and　accelerate　the　learning　speed　of　the　electronic　synapse,　which　might　open　up　a　route　for　modifying　the　characteristics　of　an　electronic　synapse.　Synaptic　learning　and　forgetting　behavior　are　effectively　simulated　with　re-learning　of　forgotten　information　at　a　much　faster　rate.