Conventional architectures face von Neumann bottleneck and surging AI energy demands. While neuromorphic computing addresses these limitations, efficient on-chip learning remains challenging. Here we present an integrated neural platform combining modulation-optimized presynaptic transistors, threshold switching memristor neurons, and adaptive feedback synapses. This system implements real-time Hebbian learning through correlation-based synaptic weight updates without complex peripheral circuitry. Comprehensive characterization of a 6×6 array confirms stable device operation and successful execution of local learning rules. Experimental results demonstrate clear correlation between input-output patterns and subsequent weight modifications, validating hardware implementation of Hebbian plasticity and establishing a pathway for autonomous on-chip learning in neuromorphic systems.