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Introducing “Neurobiological Vector Space” (𝒩): Why Biological Neurons Store Information as Directional Vectors and How the Mathematics of Artificial Intelligence Governs the Unimind–Unibrain Architecture
Authors
1 Managing Director, Bharat NeuroTech; Mental Health Specialist, Apollo Clinics (Lucknow & Gorakhpur); Ex-Medical Specialist (Psychologist), Indian Air Force; Independent Researcher based in Lucknow, Uttar Pradesh, India. Presently learning AI from IIT Kharagpur.
Article Information
- DOI
- No Doi
- Published In
- Year 2026 · Volume 1 · Issue 3
- Published Online
- September-December 2026
- Pages
- 20-34
- Article Type
- Original Article
- Check for updates
-
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 &
Engineering Technology (IJRASET), 8(IV), 399–405. doi: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 & Engineering Technology (IJRASET), 8(V), 1885–1888. doi:10.22214/ijraset.2020.5304
3. 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 (INDJCMR), 1(2), 22–29.
fdrpjournals.org/indjcmr
4. 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
(INDJCMR), 1(2), 34–47. PDF Archive
5. 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 (INDJCMR), 1(2), 48–61. fdrpjournals.org/indjcmr
6. Sodhi, N. S. (2026). Introducing Software Neurotechnology for Artificial Intelligence. Indian Journal of Computer Science and
Technology (INDJCST), 5(2), 819–827. doi:10.59256/indjcst.20260502087
7. Sodhi, N. S. (2026). 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 (INDJCMR), 1(3), 03–16.
fdrpjournals.org/indjcmr
8. 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 (INDJCMR), 1(2), 11–21. fdrpjournals.org/indjcmr
9. 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 & Engineering Technology (IJRASET), 9(VII), 1338–1339. doi:10.22214/ijraset.2021.36618
10. 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 & Engineering Technology (IJRASET),
10(VI), 3652–3655. doi:10.22214/ijraset.2022.44709
11. 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 & Engineering Technology
(IJRASET), 10(XI), 94–97. doi:10.22214/ijraset.2022.47219
12. 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 & Engineering Technology (IJRASET), 11(III), 1245–1266.
doi:10.22214/ijraset.2023.49645
13. Sodhi, N. S. (2024). Neuropsychology of Self-Regulation: Attention Control Mechanism for Controlling Thinking and Other Cognitive
Processes. International Journal for Multidisciplinary Research (IJFMR), 6(2). doi:10.36948/ijfmr.2024.v06i02.15551
14. 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.
15. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics,
5, 115–133. doi:10.1007/BF02478259
16. Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review,
65(6), 386–408. doi:10.1037/h0042519
17. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
doi:10.1038/323533a0
18. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. doi:10.1038/nature14539
19. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need.Advances in Neural Information Processing Systems (NeurIPS), 30, 5998–6008.
20. Gu, A., & Dao, T. (2024). Mamba: Linear-Time Sequence Modeling with Selective State Spaces. Proceedings of the First Conference on
Language Modeling, arXiv:2312.00752.
21. Lahoti, A., Li, K. Y., Chen, B., Wang, C., Bick, A., Kolter, J. Z., et al. (2026). Mamba-3: Improved Sequence Modeling Using State Space
Principles. International Conference on Learning Representations (ICLR), arXiv:2603.15569.
22. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., et al. (2020). Retrieval-Augmented Generation for Knowledge-
Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459–9474.
23. Xu, W., Liang, Z., Mei, K., Gao, H., Tan, J., & Zhang, Y. (2025). A-MEM: Agentic Memory for LLM Agents. Advances in Neural
Information Processing Systems (NeurIPS), 38.
24. Zhang, G., Fu, M., Wang, K., Wan, F., Yu, M., & Yan, S. (2025). G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems.
Advances in Neural Information Processing Systems (NeurIPS), 38.
25. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017). Outrageously Large Neural Networks: The
Sparsely-Gated Mixture-of-Experts Layer. International Conference on Learning Representations (ICLR).
26. Fedus, W., Zoph, B., & Shazeer, N. (2022). Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient
Sparsity. Journal of Machine Learning Research, 23(120), 1–39.
27. 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 (NeurIPS), 35.
28. 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 (NeurIPS), 36.
29. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems
(NeurIPS), 33, 6840–6851.
30. Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. (2025). Mastering diverse control tasks through world models. Nature, 640, 647–653.
doi:10.1038/s41586-025-08744-2
31. Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., et al. (2020). Mastering Atari, Go, chess and shogi by
planning with a learned model. Nature, 588, 604–609. doi:10.1038/s41586-020-03051-4
32. Frankle, J., & Carbin, M. (2019). The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. International Conference
on Learning Representations (ICLR).
33. Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., et al. (2022). Language Models (Mostly) Know What They Know.
arXiv:2207.05221.
34. Gidon, A., Zolnik, T. A., Fidzinski, P., Bolduan, F., Papoutsi, A., Poirazi, P., et al. (2020). Dendritic action potentials and computation in
human layer 2/3 cortical neurons. Science, 367(6473), 83–87. doi:10.1126/science.aax6239
35. Beniaguev, D., Segev, I., & London, M. (2021). Single cortical neurons as deep artificial neural networks. Neuron, 109(17), 2727–2739.e3.
doi:10.1016/j.neuron.2021.07.002
36. Chavlis, S., & Poirazi, P. (2025). Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.
Nature Communications, 16, 943. doi:10.1038/s41467-025-56297-9
37. Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020). Computation Through Neural Population Dynamics. Annual Review of
Neuroscience, 43, 249–275. doi:10.1146/annurev-neuro-092619-094115
38. Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018). What Is a
Cognitive Map? Organizing Knowledge for Flexible Behavior. Neuron, 100(2), 490–509. doi:10.1016/j.neuron.2018.10.002
39. Constantinescu, A. O., O’Reilly, J. X., & Behrens, T. E. J. (2016). Organizing conceptual knowledge in humans with a gridlike code.
Science, 352(6292), 1464–1468. doi:10.1126/science.aaf0941
40. Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptivefield
effects. Nature Neuroscience, 2(1), 79–87. doi:10.1038/4580
41. Miller, E. K., & Cohen, J. D. (2001). An Integrative Theory of Prefrontal Cortex Function. Annual Review of Neuroscience, 24, 167–202.
doi:10.1146/annurev.neuro.24.1.167
42. Schultz, W. (2016). Dopamine reward prediction-error signalling: a two-component response. Nature Reviews Neuroscience, 17(3), 183–
195. doi:10.1038/nrn.2015.26
43. Tononi, G., & Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and
integration. Neuron, 81(1), 12–34. doi:10.1016/j.neuron.2013.12.025
44. de Vivo, L., Bellesi, M., Marshall, W., Bushong, E. A., Ellisman, M. H., Tononi, G., & Cirelli, C. (2017). Ultrastructural evidence for
synaptic scaling across the wake–sleep cycle. Science, 355(6324), 507–510. doi:10.1126/science.aah5982
45. Modha, D. S., Akopyan, F., Andreopoulos, A., Appuswamy, R., Arthur, J., Cassidy, A., et al. (2023). Neural inference at the frontier of
energy, space, and time. Science, 382(6668), 329–335. doi:10.1126/science.adh1174
46. Davies, M., Wild, A., Orchard, G., Sandamirskaya, Y., Guerra, G. A., Joshi, P., et al. (2021). Advancing Neuromorphic Computing With
Loihi: A Survey of Results and Outlook. Proceedings of the IEEE, 109(5), 911–934. doi:10.1109/JPROC.2021.3067593
47. Sun, P., Su, Z., Achterberg, J., Indiveri, G., Goodman, D. F. M., & Akarca, D. (2026). Algorithm–hardware co-design of neuromorphic
networks with dual memory pathways. Nature Machine Intelligence, 8, 901–912. doi:10.1038/s42256-026-01255-3
48. Zhou, Y., Shah, S., Dey, T., Zhou, Y., Kumar, A., Sriram, S., et al. (2026). Protonic nickelate device networks for spatiotemporal
neuromorphic computing. Nature Nanotechnology, 21, 579–587. doi:10.1038/s41565-026-02133-0
and Provides a Novel Treatment Methodology for Mental Disorders. International Journal for Research in Applied Science &
Engineering Technology (IJRASET), 8(IV), 399–405. doi: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 & Engineering Technology (IJRASET), 8(V), 1885–1888. doi:10.22214/ijraset.2020.5304
3. 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 (INDJCMR), 1(2), 22–29.
fdrpjournals.org/indjcmr
4. 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
(INDJCMR), 1(2), 34–47. PDF Archive
5. 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 (INDJCMR), 1(2), 48–61. fdrpjournals.org/indjcmr
6. Sodhi, N. S. (2026). Introducing Software Neurotechnology for Artificial Intelligence. Indian Journal of Computer Science and
Technology (INDJCST), 5(2), 819–827. doi:10.59256/indjcst.20260502087
7. Sodhi, N. S. (2026). 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 (INDJCMR), 1(3), 03–16.
fdrpjournals.org/indjcmr
8. 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 (INDJCMR), 1(2), 11–21. fdrpjournals.org/indjcmr
9. 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 & Engineering Technology (IJRASET), 9(VII), 1338–1339. doi:10.22214/ijraset.2021.36618
10. 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 & Engineering Technology (IJRASET),
10(VI), 3652–3655. doi:10.22214/ijraset.2022.44709
11. 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 & Engineering Technology
(IJRASET), 10(XI), 94–97. doi:10.22214/ijraset.2022.47219
12. 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 & Engineering Technology (IJRASET), 11(III), 1245–1266.
doi:10.22214/ijraset.2023.49645
13. Sodhi, N. S. (2024). Neuropsychology of Self-Regulation: Attention Control Mechanism for Controlling Thinking and Other Cognitive
Processes. International Journal for Multidisciplinary Research (IJFMR), 6(2). doi:10.36948/ijfmr.2024.v06i02.15551
14. 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.
15. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics,
5, 115–133. doi:10.1007/BF02478259
16. Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review,
65(6), 386–408. doi:10.1037/h0042519
17. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
doi:10.1038/323533a0
18. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. doi:10.1038/nature14539
19. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need.Advances in Neural Information Processing Systems (NeurIPS), 30, 5998–6008.
20. Gu, A., & Dao, T. (2024). Mamba: Linear-Time Sequence Modeling with Selective State Spaces. Proceedings of the First Conference on
Language Modeling, arXiv:2312.00752.
21. Lahoti, A., Li, K. Y., Chen, B., Wang, C., Bick, A., Kolter, J. Z., et al. (2026). Mamba-3: Improved Sequence Modeling Using State Space
Principles. International Conference on Learning Representations (ICLR), arXiv:2603.15569.
22. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., et al. (2020). Retrieval-Augmented Generation for Knowledge-
Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459–9474.
23. Xu, W., Liang, Z., Mei, K., Gao, H., Tan, J., & Zhang, Y. (2025). A-MEM: Agentic Memory for LLM Agents. Advances in Neural
Information Processing Systems (NeurIPS), 38.
24. Zhang, G., Fu, M., Wang, K., Wan, F., Yu, M., & Yan, S. (2025). G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems.
Advances in Neural Information Processing Systems (NeurIPS), 38.
25. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017). Outrageously Large Neural Networks: The
Sparsely-Gated Mixture-of-Experts Layer. International Conference on Learning Representations (ICLR).
26. Fedus, W., Zoph, B., & Shazeer, N. (2022). Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient
Sparsity. Journal of Machine Learning Research, 23(120), 1–39.
27. 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 (NeurIPS), 35.
28. 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 (NeurIPS), 36.
29. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems
(NeurIPS), 33, 6840–6851.
30. Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. (2025). Mastering diverse control tasks through world models. Nature, 640, 647–653.
doi:10.1038/s41586-025-08744-2
31. Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., et al. (2020). Mastering Atari, Go, chess and shogi by
planning with a learned model. Nature, 588, 604–609. doi:10.1038/s41586-020-03051-4
32. Frankle, J., & Carbin, M. (2019). The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. International Conference
on Learning Representations (ICLR).
33. Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., et al. (2022). Language Models (Mostly) Know What They Know.
arXiv:2207.05221.
34. Gidon, A., Zolnik, T. A., Fidzinski, P., Bolduan, F., Papoutsi, A., Poirazi, P., et al. (2020). Dendritic action potentials and computation in
human layer 2/3 cortical neurons. Science, 367(6473), 83–87. doi:10.1126/science.aax6239
35. Beniaguev, D., Segev, I., & London, M. (2021). Single cortical neurons as deep artificial neural networks. Neuron, 109(17), 2727–2739.e3.
doi:10.1016/j.neuron.2021.07.002
36. Chavlis, S., & Poirazi, P. (2025). Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.
Nature Communications, 16, 943. doi:10.1038/s41467-025-56297-9
37. Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020). Computation Through Neural Population Dynamics. Annual Review of
Neuroscience, 43, 249–275. doi:10.1146/annurev-neuro-092619-094115
38. Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018). What Is a
Cognitive Map? Organizing Knowledge for Flexible Behavior. Neuron, 100(2), 490–509. doi:10.1016/j.neuron.2018.10.002
39. Constantinescu, A. O., O’Reilly, J. X., & Behrens, T. E. J. (2016). Organizing conceptual knowledge in humans with a gridlike code.
Science, 352(6292), 1464–1468. doi:10.1126/science.aaf0941
40. Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptivefield
effects. Nature Neuroscience, 2(1), 79–87. doi:10.1038/4580
41. Miller, E. K., & Cohen, J. D. (2001). An Integrative Theory of Prefrontal Cortex Function. Annual Review of Neuroscience, 24, 167–202.
doi:10.1146/annurev.neuro.24.1.167
42. Schultz, W. (2016). Dopamine reward prediction-error signalling: a two-component response. Nature Reviews Neuroscience, 17(3), 183–
195. doi:10.1038/nrn.2015.26
43. Tononi, G., & Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and
integration. Neuron, 81(1), 12–34. doi:10.1016/j.neuron.2013.12.025
44. de Vivo, L., Bellesi, M., Marshall, W., Bushong, E. A., Ellisman, M. H., Tononi, G., & Cirelli, C. (2017). Ultrastructural evidence for
synaptic scaling across the wake–sleep cycle. Science, 355(6324), 507–510. doi:10.1126/science.aah5982
45. Modha, D. S., Akopyan, F., Andreopoulos, A., Appuswamy, R., Arthur, J., Cassidy, A., et al. (2023). Neural inference at the frontier of
energy, space, and time. Science, 382(6668), 329–335. doi:10.1126/science.adh1174
46. Davies, M., Wild, A., Orchard, G., Sandamirskaya, Y., Guerra, G. A., Joshi, P., et al. (2021). Advancing Neuromorphic Computing With
Loihi: A Survey of Results and Outlook. Proceedings of the IEEE, 109(5), 911–934. doi:10.1109/JPROC.2021.3067593
47. Sun, P., Su, Z., Achterberg, J., Indiveri, G., Goodman, D. F. M., & Akarca, D. (2026). Algorithm–hardware co-design of neuromorphic
networks with dual memory pathways. Nature Machine Intelligence, 8, 901–912. doi:10.1038/s42256-026-01255-3
48. Zhou, Y., Shah, S., Dey, T., Zhou, Y., Kumar, A., Sriram, S., et al. (2026). Protonic nickelate device networks for spatiotemporal
neuromorphic computing. Nature Nanotechnology, 21, 579–587. doi:10.1038/s41565-026-02133-0
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