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

1. Sodhi NS. 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 & Engineering Technology.
2020;8(IV):399-405. doi:10.22214/ijraset.2020.4063.
2. Sodhi NS. 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 & Engineering Technology. 2020;8(V):1885-1888. doi:10.22214/ijraset.2020.5304.
3. Sodhi NS. 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. 2026;1(2):22-29.
4. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics. 1943;5:115-
133. doi:10.1007/BF02478259.
5. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review.1958;65(6):386-408. doi:10.1037/h0042519.
6. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323:533-536.
doi:10.1038/323533a0.
7. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-444. doi:10.1038/nature14539.
8. Laird JE, Lebiere C, Rosenbloom PS. A Standard Model of the Mind: Toward a Common Computational Framework across Artificial
Intelligence, Cognitive Science, Neuroscience, and Robotics. AI Magazine. 2017;38(4):13-26. doi:10.1609/aimag.v38i4.2744.
9. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995;20:273-297. doi:10.1007/BF00994018.
10. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation. 1997;9(8):1735-1780. doi:10.1162/neco.1997.9.8.1735.
11. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention Is All You Need. Advances in Neural Informat ion
Processing Systems. 2017;30:5998-6008.
12. Gu A, Dao T. Mamba: Linear-Time Sequence Modeling with Selective State Spaces. Proceedings of the First Conference on Language
Modeling. 2024. arXiv:2312.00752.
13. Lahoti A, Li KY, Chen B, Wang C, Bick A, Kolter JZ, et al. Mamba-3: Improved Sequence Modeling Using State Space Principles.
International Conference on Learning Representations. 2026. arXiv:2603.15569.
14. Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, et al. Relational inductive biases, deep learning,
and graph networks. 2018. arXiv:1806.01261.
15. Alayrac JB, Donahue J, Luc P, Miech A, Barr I, Hasson Y, et al. Flamingo: a Visual Language Model for Few-Shot Learning. Advances
in Neural Information Processing Systems. 2022;35:23716-23736.
16. Driess D, Xia F, Sajjadi MSM, Lynch C, Chowdhery A, Ichter B, et al. PaLM-E: An Embodied Multimodal Language Model. Proceedings
of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research. 2023;202:8469-8488.
17. Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.
Advances in Neural Information Processing Systems. 2020;33:9459-9474.
18. Graves A, Wayne G, Danihelka I. Neural Turing Machines. 2014. arXiv:1410.5401.
19. Xu W, Liang Z, Mei K, Gao H, Tan J, Zhang Y. A-MEM: Agentic Memory for LLM Agents. Advances in Neural Information Processing
Systems. 2025;38.
20. Zhang G, Fu M, Wang K, Wan F, Yu M, Yan S. G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems. Advances in Neural
Information Processing Systems. 2025;38.
21. Shazeer N, Mirhoseini A, Maziarz K, Davis A, Le Q, Hinton G, et al. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-
of-Experts Layer. International Conference on Learning Representations. 2017.
22. Fedus W, Zoph B, Shazeer N. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity. Journal of
Machine Learning Research. 2022;23(120):1-39.
23. Yao S, Zhao J, Yu D, Du N, Shafran I, Narasimhan K, et al. ReAct: Synergizing Reasoning and Acting in Language Models. International
Conference on Learning Representations. 2023.
24. Schick T, Dwivedi-Yu J, Dessi R, Raileanu R, Lomeli M, Hambro E, et al. Toolformer: Language Models Can Teach Themselves to Use
Tools. Advances in Neural Information Processing Systems. 2023;36:68539-68551.
25. Trinh TH, Wu Y, Le QV, He H, Luong T. Solving olympiad geometry without human demonstrations. Nature. 2024;625:476-482.
doi:10.1038/s41586-023-06747-5.
26. Manhaeve R, Dumancic S, Kimmig A, Demeester T, De Raedt L. DeepProbLog: Neural Probabilistic Logic Programming. Advances in
Neural Information Processing Systems. 2018;31.
27. Ho J, Jain A, Abbeel P. Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems. 2020;33:6840-
6851.
28. Hafner D, Pasukonis J, Ba J, Lillicrap T. Mastering diverse control tasks through world models. Nature. 2025;640:647-653.
doi:10.1038/s41586-025-08744-2.
29. Schrittwieser J, Antonoglou I, Hubert T, Simonyan K, Sifre L, Schmitt S, et al. Mastering Atari, Go, chess and shogi by planning with a
learned model. Nature. 2020;588:604-609. doi:10.1038/s41586-020-03051-4.
30. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, et al. Human-level control through deep reinforcement learning.
Nature. 2015;518:529-533. doi:10.1038/nature14236.
31. Silver D, Hubert T, Schrittwieser J, Antonoglou I, Lai M, Guez A, et al. A general reinforcement learning algorithm that masters chess,
shogi, and Go through self-play. Science. 2018;362(6419):1140-1144. doi:10.1126/science.aar6404.
32. Frankle J, Carbin M. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. International Conference on Learning
Representations. 2019.
33. Zoph B, Le QV. Neural Architecture Search with Reinforcement Learning. International Conference on Learning Representations. 2017.
34. Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, et al. Overcoming catastrophic forgetting in neural networks.
Proceedings of the National Academy of Sciences of the United States of America. 2017;114(13):3521-3526.
doi:10.1073/pnas.1611835114.
35. Chavlis S, Poirazi P. Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning. Nature
Communications. 2025;16:943. doi:10.1038/s41467-025-56297-9.
36. Modha DS, Akopyan F, Andreopoulos A, Appuswamy R, Arthur J, Cassidy A, et al. Neural inference at the frontier of energy, space, and
time. Science. 2023;382(6668):329-335. doi:10.1126/science.adh1174.
37. Davies M, Wild A, Orchard G, Sandamirskaya Y, Guerra GA, Joshi P, et al. Advancing Neuromorphic Computing With Loihi: A Survey
of Results and Outlook. Proceedings of the IEEE. 2021;109(5):911-934. doi:10.1109/JPROC.2021.3067593.
38. Sun P, Su Z, Achterberg J, Indiveri G, Goodman DFM, Akarca D. Algorithm-hardware co-design of neuromorphic networks with dual
memory pathways. Nature Machine Intelligence. 2026;8:901-912. doi:10.1038/s42256-026-01255-3.
39. Zhou Y, Shah S, Dey T, Zhou Y, Kumar A, Sriram S, et al. Protonic nickelate device networks for spatiotemporal neuromorphic computing.
Nature Nanotechnology. 2026;21:579-587. doi:10.1038/s41565-026-02133-0.
40. Gidon A, Zolnik TA, Fidzinski P, Bolduan F, Papoutsi A, Poirazi P, et al. Dendritic action potentials and computation in human layer 2/3
cortical neurons. Science. 2020;367(6473):83-87. doi:10.1126/science.aax6239.
41. Beniaguev D, Segev I, London M. Single cortical neurons as deep artificial neural networks. Neuron. 2021;109(17):2727-2739.e3.
doi:10.1016/j.neuron.2021.07.00242. Vyas S, Golub MD, Sussillo D, Shenoy KV. Computation Through Neural Population Dynamics. Annual Review of Neuroscience.
2020;43:249-275. doi:10.1146/annurev-neuro-092619-094115.
43. Behrens TEJ, Muller TH, Whittington JCR, Mark S, Baram AB, Stachenfeld KL, et al. What Is a Cognitive Map? Organizing Knowledge
for Flexible Behavior. Neuron. 2018;100(2):490-509. doi:10.1016/j.neuron.2018.10.002.
44. Constantinescu AO, O'Reilly JX, Behrens TEJ. Organizing conceptual knowledge in humans with a gridlike code. Science.
2016;352(6292):1464-1468. doi:10.1126/science.aaf0941.
45. Rao RPN, Ballard DH. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.
Nature Neuroscience. 1999;2(1):79-87. doi:10.1038/4580.
46. Schacter DL, Addis DR. The cognitive neuroscience of constructive memory: remembering the past and imagining the future. Philosophical
Transactions of the Royal Society B: Biological Sciences. 2007;362(1481):773-786. doi:10.1098/rstb.2007.2087.
47. Schultz W. Dopamine reward prediction-error signalling: a two-component response. Nature Reviews Neuroscience. 2016;17(3):183-195.
doi:10.1038/nrn.2015.26.
48. Tononi G, Cirelli C. Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration.
Neuron. 2014;81(1):12-34. doi:10.1016/j.neuron.2013.12.025.
49. de Vivo L, Bellesi M, Marshall W, Bushong EA, Ellisman MH, Tononi G, et al. Ultrastructural evidence for synaptic scaling across the
wake-sleep cycle. Science. 2017;355(6324):507-510. doi:10.1126/science.aah5982.