Abstract: This study develops hybrid ionic electrolytes by blending EMIM-TFSI, EMIM-BF₄, and EMIM-PF₆ with a polymer electrolyte (poly(DADMATFSI), PIL) to enhance the performance of organic electrochemical transistors (OECTs) for neuromorphic computing. The primary objective is to improve ionic transport speed and non-volatility in OECTs, thereby enabling better emulation of synaptic functions. Device performance was systematically evaluated through electrical characterization techniques, electrochemical impedance spectroscopy (EIS), and multilayer perceptron (MLP) simulations. Results demonstrate that hybrid electrolytes based on EMIM cations significantly enhance ionic mobility, with electrolytes containing EMIM-TFSI exhibiting superior overall performance. OECTs incorporating EMIM-TFSI-based electrolytes displayed rapid ionic transport and robust non-volatile properties, effectively mimicking synaptic plasticity. In a handwritten digit recognition task, the OECT device employing EMIM-TFSI-modified electrolytes achieved an accuracy of 84.2%, surpassing the other two electrolyte systems by 19.1% and 12.7%, respectively. This work provides valuable insights and methodologies for advancing high-performance artificial synaptic OECTs, holding promising implications for future neuromorphic computing technologies.