Contemporary neuroscience increasingly characterizes perception as arising from intrinsically generative neural activity. Yet a persistent mechanistic gap remains: how does high-entropy, spontaneously generated cortical dynamics become stabilized into discrete, reliable, and meaning-ready perceptual entities? This work presents a biologically constrained in silico implementation of the Hallucinatory Domestication Theory (HDT), explicitly modeling the transformation from generative neural chaos to operational meaning. The model integrates inhibitory stabilization, low-dimensional attractor dynamics, oscillatory temporal organization, and synchrony-based coordination within a unified computational architecture. Using systematic ablation and dose-controlled simulations, we demonstrate a clear division of labor between neural mechanisms. Attractor dynamics are shown to be necessary for content formation, inhibitory control for stabilizing generative activity into rate-coded existence, while oscillations and synchrony implement temporal syntax and coordination rather than semantic content. Meaning collapses when content-forming mechanisms are removed, but remains robust under disruption of oscillatory framing or synchrony at the local level. These results provide a mechanistic resolution to long-standing debates surrounding the role of neural oscillations and synchrony, reframing them as organizational rather than generative processes. The presented model offers a minimal yet biologically grounded substrate for studying meaning formation, temporal coordination, and their dissociation in both neuroscience and artificial intelligence. The accompanying simulation package is fully executable and designed for direct experimental engagement, ablation, and extension.