Minimal biophysical rules are sufficient for the emergence of computational intelligence at the neuronal scale

How intelligence emerges from the brains complex microscopic physical system is a central question for neuroscience and artificial intelligence. Constrained by the genomic bottleneck that precludes synapse-by-synapse specification, we propose and validate a microscopic structure-function concise-constraint sufficiency hypothesis. We develop the Neuro-Informed Generative Connectome (NIGC) framework, and show that connectomes generated under a concise set of biophysical constraints (geometric embedding, node propensity modulation, a global energy budget and maximum-entropy selection) closely match the structural statistics of a measured mouse V1 microcircuit (similarities, 0.997). In parallel, using the generated connectome as the fixed reservoir of an echo state network (ESN), training only a linear readout achieves 90% accuracy on an auditory multi-classification task. Moreover, multidimensional biologically consistent functional phenotypes, such as hierarchical transmission delays and low-dimensional spatiotemporal trajectories, are obtained without fitting functional matrices or time courses. Further, by combining single-constraint ablations, pathological perturbations and cross-modal validation, we clarify how specific structural constraints map onto functional consequences. Together, these results delineate sufficient conditions for computational intelligence emergence at the microscopic scale, and provide an auditable benchmark for first-principles understanding of brain construction.