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Original Article

The Unimind-Unibrain Double-Convergence Thesis: Why Major Artificial-Intelligence Architectures Tend Toward the Same Software-Hardware Metamodel Emerging from Contemporary Neuroscience

Dr. Nitnem Singh Sodhi1

1 Doctorate (Hon.) in Neuropsychology; Fellowship-trained in Clinical Psychiatry, Affiliation: Ex-Medical Specialist (Psychologist) at Indian Air Force and presently at Apollo Clinics, and Independent Researcher based in Lucknow, Uttar Pradesh, India.

Published Online: May-August 2026

Pages: 34-47

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Abstract

Background: Artificial neural networks arose from simplified models of biological neural computation, but artificial intelligence (AI) now includes symbolic, probabilistic, neural, generative, agentic, evolutionary, and neuromorphic paradigms. Meanwhile, contemporary neuroscience has increasingly described biological intelligence in terms of distributed state dynamics, nonlinear neuronal computation, cognitive maps, associative memory, predictive processing, valuation, plasticity, and sleep-dependent reorganization. Objective: This paper evaluates whether these two trajectories constitute a double convergence upon the Unimind Metamodel as a software-level account of intelligence and the Unibrain Theory as a hardware-level account. Methods: A theory-led integrative review was conducted across representative AI architecture families and relevant neuroscience and neuromorphic literature available through July 2026. Mappings were accepted only when an architecture implemented an operationally comparable function, not merely similar terminology. Results: Across architecture families, increasing generality repeatedly adds the same functional requirements: a structured representational map, objects or entities, associative memory, a repertoire of algorithms, relevance- based gating, prediction and internal simulation, valuation, an operational self-model, learning-driven reconfiguration, offline consolidation, and a resource-constrained physical substrate. Transformers, state-space models, graph networks, retrieval systems, world models, reinforcement learning, neuro-symbolic systems, mixtures of experts, tool-using agents, continual-learning systems, dendritic networks, and neuromorphic hardware each instantiate subsets of this organization and are increasingly integrated. Conclusion: The evidence does not establish literal identity, direct historical influence, consciousness in machines, or exclusive priority for every component. It supports a stronger and testable claim: under partial observability, long horizons, changing environments, goal-directed action, and finite resources, the Unimind-Unibrain organization is a candidate architectural attractor. Convergence is therefore evident as an integration trend and conditionally inevitable at the level of functional problems that any open-ended adaptive intelligence must solve.

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Citations

Dr. Nitnem Singh Sodhi, “The Unimind-Unibrain Double-Convergence Thesis: Why Major Artificial-Intelligence Architectures Tend Toward the Same Software-Hardware Metamodel Emerging from Contemporary Neuroscience”, Indian Journal of Clinical and Medical Research, Volume 01, Issue 02, May-August 2026, PP: 34-47.

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© 2026 The Author(s). Published by Fifth Dimension Research Publication.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

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