Adaptive Resonance Theory (ART) neural network with bidirectional F1 input and F2 recognition layers connected via bottom-up and top-down weighted pathways. External input feeds the F1 layer while gain control and reset mechanisms regulate network dynamics based on vigilance parameter rho. The architecture demonstrates unsupervised learning through resonance matching between input patterns and learned prototypes, enabling stable category learning without catastrophic forgetting. Fork this diagram on Diagrams.so to customize layer sizes, weight configurations, or add additional control modules for your ART implementation. The reset module's mismatch detection and vigilance threshold control are critical for balancing plasticity and stability in pattern recognition tasks.