Currently, analog in-memory computing, employing memristors into a crossbar array architecture (CAA), is the leading system among available neuromorphic hardware. This study presents a highly tunable synaptic weight update based on a multiterminal memtransistor device as a solution for nonlinear synaptic operations and crosstalk issues in CAA memristors, which are long-standing challenges in neuromorphic hardware applications. To explore an effective device structure for tunable weight update properties, a memtransistor device with a series and parallel structure functioning by interface type and oxygen migration is fabricated using a ZnO channel layer and an amorphous TiO2 memristor. The series memtransistor device exhibits a significant tunable weight update property at the gate knob; thus, it simultaneously can function as a selector (accelerating and inhibiting weight update) in the CAA and tune and ultimately improve the linearity of the potentiation and depression curves. Neuromorphic hardware based on tunable synaptic weight update functions provides advantageous features for accuracy and crosstalk issues. Using the Fashion-MNIST pattern recognition simulation, the tuned weight update properties are obtained by three different write and read condition combinations, and the results are close to ideal accuracy.