Dr. Sodhi's Model of Intelligence-An Empirically Validated Unified Software-Hardware Architecture across Biological and Artificial Intelligence

DOI

No Doi

Published In

Year 2026 · Volume 1 · Issue 2

Published Online

May-August 2026

Pages

48-61

Article Type

Original Article

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Research on intelligence remains divided among behavioral definitions, cognitive architectures, computational principles, and physical implementations. Building on the Unimind Metamodel and the Unibrain Theory, this paper formalizes the Dr. Sodhi's Model of Intelligence (SMI): a unified cross-substrate architecture in which intelligence is produced by the coupling of functional organization, physical realization, and environment-directed interaction. SMI specifies representational state, associative memory, reusable operations, relevance-based gating, prediction and counterfactual simulation, valuation, operational self-modeling, learning, and consolidation, together with the computational units, state dynamics, connectivity, signaling, storage, plasticity, and resource budgets that physically realize them. The model is already empirically validated in a defined convergent sense: independent findings in neural population dynamics, dendritic computation, cognitive mapping, predictive processing, selective attention, neuromodulation, plasticity, and sleep-dependent consolidation support its biological postulates, while transformers, graph networks, retrieval systems, external memory, mixtures of experts, world models, continual-learning methods, and neuromorphic hardware independently instantiate its principal artificial-system roles. This is validation of the component architecture, its cross-level correspondence, and its recurrence across substrates; it is not a claim that one experiment has proved the universal necessity of the complete conjunction. The strongest remaining proposition is conditional: systems pursuing persistent, open-ended adaptive competence under partial observability, non-stationarity, long horizons, multiple objectives, and finite resources must instantiate functional equivalents of the complete architecture, although roles may be fused, distributed, or realized through different mechanisms. Narrow systems may instantiate only subsets. Functional correspondence does not establish biological identity, consciousness, or moral equivalence. Causal ablation, cross-substrate perturbation, continual-learning, and hardware-efficiency tests keep the necessity and interaction claims open to falsification. SMI is therefore advanced as an empirically validated and prospectively testable architectural normal form for biological and artificial intelligence.