Loading...

Please wait while we retrieve your documents

ARCHIVES

Year 2026 · Volume 1 · Issue 3

Original Article

Introducing Software Neurotechnology for Humans: A Human-Led, AI-Augmented Architecture for Cognitive Extension, Agency, and Planetary Intelligence

Dr. Nitnem Singh Sodhi1
1 Managing Director: Bharat Neurotech Mental Health Specialist: Apollo Clinics Lucknow & Gorakhpur, Ex-Air Force Psychologist / Ex-UP Police Forensics Expert, Uttar Pradesh, India.

Published Online: September-December 2026

Pages: 03-16

Cite this article

No Doi
check update

Abstract

The human brain/mind has always used external tools for extending its functions. Writing extends memory, mathematics extends calculation, books extend knowledge, and computers extend information processing. Artificial Intelligence now makes possible a qualitatively different extension, because the external tool can itself remember, reason, plan, verify, learn and act in relation to the changing state of one individual. This paper hereby introduces "Software Neurotechnology for Humans" (SNH), a framework in which AI is arranged as a set of software layers around a human nervous system for extending its functional intelligence without directly modifying its biological tissue. The proposal is the human counterpart of Software Neurotechnology for Artificial Intelligence [21], in which external software layers augment a trained AI model without altering its underlying weights. The framework contains two interconnected architectures. The first is a three-layer governance architecture consisting of Vision, Goals & Planning, and Intelligence & Execution. Vision remains under human authority; generative AI assists in translating Vision into explicit goals and plans; and agentic AI supplies bounded intelligence and execution. The second is a twenty-function implementation architecture consisting of memory, reasoning, planning, self-regulation, world-modeling, learning and adaptation functions. These functions are implemented through a Personal Cognitive State, permissioned long-term memory, grounding and verification systems, a cognitive orchestrator, tool interfaces, an action gateway and an audit trail. A practical implementation method for an individual is described using presently available consumer devices and AI systems. The framework is further extended from the individual scale to the collective scale. A human connected with a persistent personal AI can be understood as a micro-level hybrid Distributed Nervous System. Networks of such individuals can form larger organizational, national and civilizational nervous systems. However, different humans and different societies do not possess one identical Vision; their values, interests and purposes can conflict. Therefore the planetary problem is not solved by creating one AI with one global purpose. This paper instead proposes a "Planetary Intelligence" composed of multiple national/civilizational nervous systems that retain their own purposes while being connected through shared world-modeling, translation, verification, conflict-mapping, simulation, negotiation and coordinated action. Planetary Intelligence is thus proposed as a polycentric meta-nervous system: not a single planetary ruler, but an architecture through which different human purposes can coexist, understand one another, discover compatible solutions and coordinate on genuinely planetary problems. The final objective of SNH is therefore not replacement of human intelligence, but extension of individual human agency and the eventual possibility of a plural, human-governed intelligence operating at planetary scale.

Previous

References

  • 1. Sodhi, N. S. (2020). The Unimind Metamodel & Therapy - An Unified Functional Framework of Mind that Explains all its Faculties and provides a Novel Treatment Methodology for Mental Disorders. International Journal for Research in Applied Science and Engineering Technology, 8(IV), 399-405. https://doi.org/10.22214/ijraset.2020.4063

  • 2. Sodhi, N. S. (2020). The Unibrain Theory - An Unified Functional Architecture of the Brain which Explains how the Brain Encodes, Stores & Processes Information, Explains Consciousness and the Function of Sleep and Dreams. International Journal for Research in Applied Science and Engineering Technology, 8(V), 1885-1888. https://doi.org/10.22214/ijraset.2020.5304

  • 3. Sodhi, N. S. (2021). Electrolyzed Water Steam Inhalation (ELWASTIN) Therapy: a novel complementary treatment for a variety of diseases including COVID-19. International Journal for Research in Applied Science and Engineering Technology, 9(IV), 1565-1566. https://doi.org/10.22214/ijraset.2021.34003

  • 4. Mishra, P., & Sodhi, N. S. (2021). The Neuro-Psychology of Learning and Fetal Programming along with a panacea for all types of learning disorders. International Journal for Research in Applied Science and Engineering Technology, 9(XI), 953-956. https://doi.org/10.22214/ijraset.2021.38939

  • 5. Sodhi, N. S. (2021). COGNISHIELD: A Simple Practical Technique for Instant Spontaneous Treatment of all Possible Psychological Disorders and to Drastically Enhance Cognition by Absolute Conscious Control of Mind. International Journal for Research in Applied Science and Engineering Technology, 9(VII), 1338-1339. https://doi.org/10.22214/ijraset.2021.36618

  • 6. Sodhi, N. S., & Mishra, P. (2021). The Unimed System (Unimedicine): A Novel System of Modern Medical Science. International Journal for Research in Applied Science and Engineering Technology, 9(XII), 204-207. https://doi.org/10.22214/ijraset.2021.39264

  • 7. Sodhi, N. S. (2022). Further developments in the Unimed System (Unimedicine). International Journal for Research in Applied Science and Engineering Technology, 10(IV), 1942-1945. https://doi.org/10.22214/ijraset.2022.41574

  • 8. Sodhi, N. S. (2022). Self-Questioning Therapy (SQT): Capable of Removing Individual Thoughts from Mind, To Be Used as First-Aid & Treatment for All Psychological Problems. International Journal for Research in Applied Science and Engineering Technology, 10(VI), 3652-3655. https://doi.org/10.22214/ijraset.2022.44709

  • 9. Sodhi, N. S. (2022). ANSAR Examination: Neurological Examination Methods to Ascertain Subconscious and Unconscious Reactions of Mind/Brain for any Specific Stimulus or Situation. International Journal for Research in Applied Science and Engineering Technology, 10(XI), 94-97. https://doi.org/10.22214/ijraset.2022.47219

  • 10. Sodhi, N. S. (2023). Unified Neural Trait Range Assessment (UNETRA): A Complete Neuro-Psychological Personality Classification and Assessment System. International Journal for Research in Applied Science and Engineering Technology, 11(III), 1245-1266. https://doi.org/10.22214/ijraset.2023.49645

  • 11. Sodhi, N. S. (2023). Gendivergence: A Comprehensive Framework for Redefining the LGBTQIA+ Spectrum and Community. International Journal for Multidisciplinary Research, 5(4). https://doi.org/10.36948/ijfmr.2023.v05i04.4297

  • 12. Sodhi, N. S. (2023). Review of Unimedicine as a Complete System of Alternative Medicine for Physical and Mental Health. International Journal for Multidisciplinary Research, 5(4). https://doi.org/10.36948/ijfmr.2023.v05i04.4696

  • 13. Sodhi, N. S. (2023). Scientific Metastructure Theory: Universal Temporal Selection Process for Increasing Entropy via Consumption Machines. International Journal for Multidisciplinary Research, 5(4). https://doi.org/10.36948/ijfmr.2023.v05i04.5988

  • 14. Sodhi, N. S. (2024). Neuropsychology of Self-regulation: Attention Control Mechanism for Controlling Thinking and Other Cognitive Processes. International Journal for Multidisciplinary Research, 6(2). https://doi.org/10.36948/ijfmr.2024.v06i02.15551

  • 15. Sodhi, N. S. (2024). The Elemental Framework of Unimedicine (Unimed System of Medicine). International Journal of Medical Science and Current Research, 7(2), 520-529.

  • 16. Sodhi, N. S. (2025). Distributed Nervous Systems: A Macro-Level Understanding of Individual and Collective Neuroscience. International Journal of Scientific Research, 14(2), 15-17.

  • 17. Sodhi, N. S. (2026). The 'END' of Psychiatry: Epigenetic Neurochemical Dysregulation (END) as the Universal Mechanism and Framework for Psychopathology. Indian Journal of Clinical and Medical Research, 1(2), 11-21.

  • 18. Sodhi, N. S. (2026). The Unified Architecture of Neuropsychology - A Comprehensive Review of the Unibrain Theory and the Unimind Metamodel with Contemporary Empirical Evidence. Indian Journal of Clinical and Medical Research, 1(2), 22-29.

  • 19. Sodhi, N. S. (2026). 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, 1(2), 34-47.

  • 20. Sodhi, N. S. (2026). Dr. Sodhi's Model of Intelligence - An Empirically Validated Unified Software-Hardware Architecture across Biological and Artificial Intelligence. Indian Journal of Clinical and Medical Research, 1(2), 48-61.

  • 21. Sodhi, N. S. (2026). Introducing Software Neurotechnology for Artificial Intelligence. Indian Journal of Computer Science and Technology, 5(2), 819-827. https://doi.org/10.59256/indjcst.20260502087

  • 22. Licklider, J. C. R. (1960). Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1, 4-11. https://doi.org/10.1109/THFE2.1960.4503259

  • 23. Engelbart, D. C. (1962). Augmenting Human Intellect: A Conceptual Framework. Stanford Research Institute, Summary Report AFOSR-3223.

  • 24. Clark, A., & Chalmers, D. (1998). The Extended Mind. Analysis, 58(1), 7-19. https://doi.org/10.1093/analys/58.1.7

  • 25. Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Sciences, 20(9), 676-688. https://doi.org/10.1016/j.tics.2016.07.002

  • 26. Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips. Science, 333(6043), 776-778. https://doi.org/10.1126/science.1207745

  • 27. Miller, E. K., & Cohen, J. D. (2001). An Integrative Theory of Prefrontal Cortex Function. Annual Review of Neuroscience, 24, 167-202. https://doi.org/10.1146/annurev.neuro.24.1.167

  • 28. Diamond, A. (2013). Executive Functions. Annual Review of Psychology, 64, 135-168. https://doi.org/10.1146/annurev-psych-113011-143750

  • 29. Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., & Frith, C. D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences, 97(8), 4398-4403. https://doi.org/10.1073/pnas.070039597

  • 30. Parasuraman, R., & Manzey, D. H. (2010). Complacency and Bias in Human Use of Automation: An Attentional Integration. Human Factors, 52(3), 381-410. https://doi.org/10.1177/001872081037605531. Liu, G., Christian, B., Dumbalska, T., Bakker, M. A., & Dubey, R. (2026). AI Assistance Reduces Persistence and Hurts Independent Performance. arXiv:2604.04721 (preprint; version 4, August 2026). https://doi.org/10.48550/arXiv.2604.04721

  • 32. Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A Model for Types and Levels of Human Interaction with Automation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 30(3), 286-297. https://doi.org/10.1109/3468.844354

  • 33. Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., et al. (2019). Guidelines for Human-AI Interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Paper 3, 1-13. https://doi.org/10.1145/3290605.3300233

  • 34. Packer, C., Wooders, S., Lin, K., Fang, V., Patil, S. G., Stoica, I., & Gonzalez, J. E. (2023). MemGPT: Towards LLMs as Operating Systems. arXiv:2310.08560.

  • 35. Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., & Yao, S. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. Advances in Neural Information Processing Systems, 36.

  • 36. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Kuttler, H., Lewis, M., Yih, W., Rocktaschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33.

  • 37. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems, 35.

  • 38. Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of Thoughts: Deliberate Problem Solving with Large Language Models. Advances in Neural Information Processing Systems, 36.

  • 39. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. International Conference on Learning Representations.

  • 40. Schick, T., Dwivedi-Yu, J., Dessi, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools. Advances in Neural Information Processing Systems, 36.

  • 41. Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., et al. (2023). Self-Refine: Iterative Refinement with Self-Feedback. Advances in Neural Information Processing Systems, 36.

  • 42. Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., & Mordatch, I. (2023). Improving Factuality and Reasoning in Language Models through Multiagent Debate. arXiv:2305.14325.

  • 43. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

  • 44. Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.

  • 45. Hubinger, E., van Merwijk, C., Mikulik, V., Skalse, J., & Garrabrant, S. (2019). Risks from Learned Optimization in Advanced Machine Learning Systems. arXiv:1906.01820.

  • 46. Kulveit, J., Douglas, R., Ammann, N., Turan, D., Krueger, D., & Duvenaud, D. (2025). Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development. arXiv:2501.16946.

  • 47. Strathern, M. (1997). 'Improving ratings': audit in the British university system. European Review, 5(3), 305-321.

  • 48. Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakci, O., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122

  • 49. Hutchins, E. (1995). Cognition in the Wild. MIT Press. https://doi.org/10.7551/mitpress/1881.001.0001

  • 50. Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686-688. https://doi.org/10.1126/science.1193147

  • 51. Malone, T. W., & Bernstein, M. S. (Eds.). (2015). Handbook of Collective Intelligence. MIT Press.

  • 52. Ostrom, E. (2010). Polycentric systems for coping with collective action and global environmental change. Global Environmental Change, 20(4), 550-557. https://doi.org/10.1016/j.gloenvcha.2010.07.004

  • 53. Dafoe, A., Bachrach, Y., Hadfield, G., Horvitz, E., Larson, K., & Graepel, T. (2021). Cooperative AI: machines must learn to find common ground. Nature, 593, 33-36. https://doi.org/10.1038/d41586-021-01170-0

  • 54. Frank, A., Grinspoon, D., & Walker, S. (2022). Intelligence as a planetary scale process. International Journal of Astrobiology, 21(2), 47-61. https://doi.org/10.1017/S147355042100029X

  • 55. Arrow, K. J. (1963). Social Choice and Individual Values (2nd ed.). Yale University Press.

Citations

Dr. Nitnem Singh Sodhi, “Introducing Software Neurotechnology for Humans A Human-Led, AI-Augmented Architecture for Cognitive Extension, Agency, and Planetary Intelligence”, Indian Journal of Clinical and Medical Research, Volume 01, Issue 03, September-December 2026, PP: 03-16.

Article Metrics

392
Views
0
Citations
PlumX Metrics PlumX Metrics
Dimension Dimension

Licensing

© 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.

Related Articles in this Issue

Read Article
Read Article
Read Article
Read Article
Read Article
Read Article