Neuromorphic　computing,　inspired　by　the　intricate　neural　networks　of　the　human　brain,　seeks　to　transcend　the　limitations　of　conventional　computing　by　integrating　information　processing　and　storage.　Central　to　this　paradigm　shift　are　artificial　synaptic　devices,　the　cornerstone　of　neuromorphic　systems,　which　excel　in　energy　efficiency　by　facilitating　low-power　signal　processing.　This　attribute　has　garnered　substantial　interest　within　the　artificial　intelligence　domain.　A　pivotal　challenge　in　neuromorphic　computing　is　the　precise　modulation　of　synaptic　performance　through　innovative　device　architectures.　This　is　vital　for　realizing　neural　computations　with　high　precision　and　fidelity.　Among　the　various　strategies　explored,　bulk　heterojunctions　have　garnered　significant　attention　due　to　their　distinctive　structural　and　functional　attributes,　which　offer　precision　and　potential　in　fine-tuning　synaptic　behavior.　In　this　work,　a　polymer-co-mixed　bulk　heterojunction　transistor　is　proposed,　which　has　been　fabricated　by　merely　combining　a　p-type　semiconductor,　which　has　an　excess　of　positive　charge　carriers,　with　an　n-type　semiconductor,　which　has　an　excess　of　negative　charge　carriers,　through　a　simple　mixing　process　without　the　use　of　an　additional　capture　layer,　resulting　in　the　fabrication　of　a　bulk　heterojunction　organic　synaptic　transistor　(BHJ-OST)　with　signalling　and　self-learning　properties　and　a　large　storage　window　of　more　than　60V.　In　addition,　the　device　successfully　mimics　biological　synaptic　properties,　including　excitatory　postsynaptic　current　(EPSC),　inhibitory　postsynaptic　current　(IPSC),　and　long-range　plasticity　(LTP).　©　2025　IEEE.