This article presents a comprehensive review of Jneopallium, a Java-based open-source framework for modeling natural neuron networks at user-selected levels of biological detail. Originally introduced in IJSR 13(7), 2024, the framework has since matured into a multi-module platform that combines four immutable core abstractions — typed signals, neuron interfaces with multiple receptors, stateless signal processors, and a dual fast/slow processing-loop scheduler — with fifteen domain modules spanning autonomous-AI safety (harm discriminator, loop circuit-breakers), biological subsystems (affect, embodiment, curiosity, glia, sleep), an optional Large Language Model advisory layer, and six application-domain implementations (brain-computer interfaces, clinical decision support, cybersecurity, industrial process control, swarm robotics, and adaptive tutoring). We trace the historical lineage of the idea from Hebb's 1949 learning rule through the Farley-Clark 1954 simulation, Rosenblatt's perceptron, Hubel-Wiesel's visual cortex work, Fukushima's neocognitron, Kohonen's self-organizing maps, and the deep-learning era to the present day. We compare Jneopallium with the closest competitors — NEURON Simulator, CoreNeuron, NEST, Brian2, and Nengo — and discuss why typed-signal, multi-receptor, multi-timescale architectures fill a gap that neither high-detail biophysical simulators nor matrix-oriented deep-learning frameworks address. Finally, we estimate the economic impact across robotics, healthcare, energy, defense, and education, and outline directions for future research and deployment.