Scilight Press

Author Information

Received: 19 October 2026 | Accepted: 27 October 2026 | Published: 06 November 2026

References 

• 1.

  Emery, N.J.; Clayton, N.S. The Mentality of Crows: Convergent Evolution of Intelligence in Corvids and Apes. Science 2004, 306, 1903–1907. https://doi.org/10.1126/science.1098410.

• 2.

  McCulloch, W.S.; Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 1943, 5, 115–133.

• 3.

  Hebb, D.O. The Organization of Behavior: A Neuropsychological Theory; Wiley: New York, NY, USA, 1949.

• 4.

  Turing, A.M. Computing Machinery and Intelligence. Mind 1950, LIX, 433–460. https://doi.org/10.1093/mind/lix.236.433.

• 5.

  McCarthy, J.; Minsky, M.L.; Rochester, N.; et al. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Proposal Dated 31 August 1955. Available online: https://www-formal.stanford.edu/jmc/history/dartmouth.pdf (accessed on 1 November 2026).

• 6.

  Rosenblatt, F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958, 65, 386–408. https://doi.org/10.1037/h0042519.

• 7.

  Hopfield, J.J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 1982, 79, 2554–2558. https://doi.org/10.1073/pnas.79.8.2554.

• 8.

  Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning representations by back-propagating errors. Nature 1986, 323, 533–536. https://doi.org/10.1038/323533a0.

• 9.

  Rescorla, R.A.; Wagner, A.R. A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Nonreinforcement. In Classical Conditioning II: Current Research and Theory; Appleton-Century-Crofts: New York, NY, USA, 1972; pp. 64–99.

• 10.

  Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet Classification with Deep Convolutional Neural Networks. Adv. Neural Inf. Process. Syst. 2012, 25, 1097–1105.

• 11.

  LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. https://doi.org/10.1038/nature14539.

• 12.

  Mnih, V.; Kavukcuoglu, K.; Silver, D.; et al. Human-level control through deep reinforcement learning. Nature 2015, 518, 529–533. https://doi.org/10.1038/nature14236.

• 13.

  Silver, D.; Huang, A.; Maddison, C.J.; et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016, 529, 484–489. https://doi.org/10.1038/nature16961.

• 14.

  Silver, D.; Hubert, T.; Schrittwieser, J.; et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 2018, 362, 1140–1144. https://doi.org/10.1126/science.aar6404.

• 15.

  Vaswani, A.; Shazeer, N.; Parmar, N.; et al. Attention Is All You Need. Adv. Neural Inf. Process. Syst. 2017, 30, 5998–6008.

• 16.

  Brown, T.B.; Mann, B.; Ryder, N.; et al. Language Models are Few-Shot Learners. In Proceedings of the Advances in Neural Information Processing Systems, Online, 6–12 December 2020; Volume 33, pp. 1877–1901.

• 17.

  Bommasani, R.; Hudson, D.A.; Adeli, E.; et al. On the Opportunities and Risks of Foundation Models. arXiv 2021, arXiv:2108.07258.

• 18.

  Hodgkin, A.L.; Huxley, A.F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 1952, 117, 500–544. https://doi.org/10.1113/jphysiol.1952.sp004764.

• 19.

  Hubel, D.H.; Wiesel, T.N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 1962, 160, 106–154. https://doi.org/10.1113/jphysiol.1962.sp006837.

• 20.

  Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information; W. H. Freeman: San Francisco, CA, USA, 1982.

• 21.

  Churchland, M.M.; Cunningham, J.P.; Kaufman, M.T.; et al. Neural population dynamics during reaching. Nature 2012, 487, 51–56. https://doi.org/10.1038/nature11129.

• 22.

  Rao, R.P.N.; Ballard, D.H. Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 1999, 2, 79–87. https://doi.org/10.1038/4580.

• 23.

  Friston, K. The free-energy principle: A unified brain theory? Nat. Rev. Neurosci. 2010, 11, 127–138.

• 24.

  Schultz, W.; Dayan, P.; Montague, P.R. A Neural Substrate of Prediction and Reward. Science 1997, 275, 1593–1599. https://doi.org/10.1126/science.275.5306.1593.

• 25.

  Miller, E.K.; Cohen, J.D. An Integrative Theory of Prefrontal Cortex Function. Annu. Rev. Neurosci. 2001, 24, 167–202. https://doi.org/10.1146/annurev.neuro.24.1.167.

• 26.

  Seth, A.K.; Bayne, T. Theories of consciousness. Nat. Rev. Neurosci. 2022, 23, 439–452. https://doi.org/10.1038/s41583-022-00587-4.

• 27.

  Ferrante, O.; Gorska-Klimowska, U.; Henin, S.; et al. Adversarial testing of global neuronal workspace and integrated information theories of consciousness. Nature 2025, 642, 133–142. https://doi.org/10.1038/s41586-025-08888-1.

• 28.

  Mathis, M.W.; Mathis, A. Joint modelling of brain and behaviour dynamics with artificial intelligence. Nat. Rev. Neurosci. 2026, 27, 87–100. https://doi.org/10.1038/s41583-025-00996-1.

• 29.

  Kriegeskorte, N.; Mur, M.; Bandettini, P.A. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2008, 2, 249. https://doi.org/10.3389/neuro.06.004.2008.

• 30.

  Yamins, D.L.K.; DiCarlo, J.J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 2016, 19, 356–365. https://doi.org/10.1038/nn.4244.

• 31.

  Wang, E.Y.; Fahey, P.G.; Ding, Z.; et al. Foundation model of neural activity predicts response to new stimulus types. Nature 2025, 640, 470–477. https://doi.org/10.1038/s41586-025-08829-y.

• 32.

  Huys, Q.J.M.; Maia, T.V.; Frank, M.J. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat. Neurosci. 2016, 19, 404–413. https://doi.org/10.1038/nn.4238.

• 33.

  Willett, F.R.; Avansino, D.T.; Hochberg, L.R.; et al. High-performance brain-to-text communication via handwriting. Nature 2021, 593, 249–254. https://doi.org/10.1038/s41586-021-03506-2.

• 34.

  Willett, F.R.; Kunz, E.M.; Fan, C.; et al. A high-performance speech neuroprosthesis. Nature 2023, 620, 1031–1036. https://doi.org/10.1038/s41586-023-06377-x.

• 35.

  Metzger, S.L.; Littlejohn, K.T.; Silva, A.B.; et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature 2023, 620, 1037–1046. https://doi.org/10.1038/s41586-023-06443-4.

• 36.

  Zador, A.; Escola, S.; Richards, B.; et al. Catalyzing next-generation Artificial Intelligence through NeuroAI. Nat. Commun. 2023, 14, 1597. https://doi.org/10.1038/s41467-023-37180-x.

• 37.

  Schuman, C.D.; Kulkarni, S.R.; Parsa, M.; et al. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2022, 2, 10–19. https://doi.org/10.1038/s43588-021-00184-y.

• 38.

  Cai, H.; Ao, Z.; Tian, C.; et al. Brain organoid reservoir computing for artificial intelligence. Nat. Electron. 2023, 6, 1032–1039. https://doi.org/10.1038/s41928-023-01069-w.

• 39.

  Kagan, B.J.; Kitchen, A.C.; Tran, N.T.; et al. In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron 2022, 110, 3952–3969.e8. https://doi.org/10.1016/j.neuron.2022.09.001.

• 40.

  Bau, D.; Zhou, B.; Khosla, A.; et al. Network Dissection: Quantifying Interpretability of Deep Visual Representations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 6541–6549. https://doi.org/10.1109/cvpr.2017.354.

• 41.

  Ross, L.N.; Bassett, D.S. Causation in neuroscience: Keeping mechanism meaningful. Nat. Rev. Neurosci. 2024, 25, 81–90. https://doi.org/10.1038/s41583-023-00778-7.

• 42.

  Glickman, M.; Sharot, T. How human–AI feedback loops alter human perceptual, emotional and social judgements. Nat. Hum. Behav. 2024, 9, 345–359. https://doi.org/10.1038/s41562-024-02077-2.

• 43.

  Messeri, L.; Crockett, M.J. Artificial intelligence and illusions of understanding in scientific research. Nature 2024, 627, 49–58. https://doi.org/10.1038/s41586-024-07146-0.

• 44.

  Castelvecchi, D. OpenAI posts 700 maths preprints online: Mathematicians are up in arms. Nature 2026. https://doi.org/10.1038/d41586-026-03196-8.

• 45.

  Alper, J.; Barany, M.J.; et al. Leiden Declaration on Artificial Intelligence and Mathematics. Available online: https://leidendeclaration.ai/ (accessed on 1 November 2026).