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Conscious…But Not Like Us: Charting the True Path of Artificial Minds

by Phenomenology TechnologyMay 13th, 2025
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AI may achieve new forms of consciousness, but clear distinctions from human consciousness and precise definitions are essential for future research.

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Abstract and Introduction

  1. Extents and ways in which AI has been inspired by understanding of the brain

    1.1 Computational models

    1.2 Artificial Neural Networks

  2. Embodiment of conscious processing: hierarchy and parallelism of nested levels of organization

  3. Evolution: from brain architecture to culture

    3.1 Genetic basis and epigenetic development of the brain

    3.2 AI and evolution: consequences for artificial consciousness

  4. Spontaneous activity and creativity

  5. Conscious vs non-conscious processing in the brain, or res cogitans vs res extensa

  6. AI consciousness and social interaction challenge rational thinking and language

Conclusion, Acknowledgments, and References

Conclusion

We have here reviewed some structural, evolutionary and functional features of the brain that have played an important role in making possible and/or modulating human consciousness (See Table 1). These features may possibly contribute to make artificial consciousness achievable. Against this background, we also identified some limitations of current computer hardware and AI models that we suggest should be improved for accelerating research towards the development of artificial consciousness (See Table 2). Even if it is theoretically feasible to develop artificial systems with non-human-like forms of consciousness, we argue that taking into account the brain features above, which are presently not fully translated into AI, may accelerate and enrich the development of conscious artificial systems. This does not mean that it is actually possible to develop a human-like artificial conscious system. In fact, there is still a long way to go to fairly emulate conscious processing in humans, if it ever will be possible. Given this uncertainty, we recommend not to use for the time being the same general term (i.e., consciousness) for both humans and artificial systems; to clearly specify the key differences between them; and, last but not least, to be very clear about which dimension, scale and level of consciousness the artificial system may possibly be capable of displaying.


Table 1. Brain features that should be taken into account for accelerating research towards the development of conscious AI


Table 2. Limitations of current AI that should be ameliorated for accelerating research towards the development of conscious AI


Acknowledgments


Special thanks to Jan Aru, Sacha J. van Albada, Ismael Freire, Mehdi Khamassi, and Mihai Petrovici for comments on a previous version of this paper, and to two anonymous reviewers for extremely useful comments that improved the readability and clarity of the paper.

References

Albantakis, Barbosa, L., Findlay, G., Grasso, M., Haun, A. M., Marshall, W., . . . Tononi, G. (2023). Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms. PLoS Comput Biol, 19(10), e1011465. doi:10.1371/journal.pcbi.1011465


Alexandre, F., Dominey, P. F., Gaussier, P., Girard, B., Khamassi, M., & Rougier, N. P. (2020). When Artificial Intelligence and Computational Neuroscience Meet. In P. Marquis, O. Papini, & H. Prade (Eds.), A Guided Tour of Artificial Intelligence Research: Volume III: Interfaces and Applications of Artificial Intelligence (pp. 303-335). Cham: Springer International Publishing.


Ali, A., & al. (2022). Predictive coding is a consequence of energy efficiency in recurrent neural networks. Patterns (N Y), 3(12), 100639. doi:10.1016/j.patter.2022.100639


Arsiwalla, X. D., Solé, R., Moulin-Frier, C., Herreros, I., Sánchez-Fibla, M., & Verschure, P. (2023). The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines. NeuroSci, 4(2), 79-102.


Aru, J., Larkum, M. E., & Shine, J. M. (2023). The feasibility of artificial consciousness through the lens of neuroscience. Trends in Neurosciences, 46(12), 1008-1017. doi:https://doi.org/10.1016/j.tins.2023.09.009


Aru, J., Suzuki, M., & Larkum, M. E. (2020). Cellular Mechanisms of Conscious Processing. Trends Cogn Sci, 24(10), 814-825. doi:10.1016/j.tics.2020.07.006


Barron, A. B., Halina, M., & Klein, C. (2023). Transitions in cognitive evolution. Proc Biol Sci, 290(2002), 20230671. doi:10.1098/rspb.2023.0671


Bartocci, M., Bergqvist, L. L., Lagercrantz, H., & Anand, K. J. (2006). Pain activates cortical areas in the preterm newborn brain. Pain, 122(1-2), 109-117. doi:10.1016/j.pain.2006.01.015


Bayne, T., Hohwy, J., & Owen, A. M. (2016). Are There Levels of Consciousness? Trends Cogn Sci, 20(6), 405- 413. doi:10.1016/j.tics.2016.03.009


Bayne, T., Seth, A. K., Massimini, M., Shepherd, J., Cleeremans, A., Fleming, S. M., . . . Mudrik, L. (2024). Tests for consciousness in humans and beyond. Trends Cogn Sci. doi:10.1016/j.tics.2024.01.010


Bennett, M. S. (2023). A brief history of intelligence : evolution, AI, and the five breakthroughs that made our brains (First edition. ed.). New York: Mariner Books.


Berger, H. (1929). Über das Elektrenkephalogramm des Menschen. Archiv für Psychiatrie und Nervenkrankheiten, 87(1), 527-570. doi:10.1007/BF01797193


Billaudelle, S., Stradmann, Y., Schreiber, K., Cramer, B., Baumbach, A., Dold, D., . . . Meier, K. (2020, 12-14 Oct 2020). Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate. Paper presented at the 2020 IEEE International Symposium on Circuits and Systems (ISCAS).


Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med, 34(4), 537-541. doi:10.1002/mrm.1910340409


Block. (1995). On a confusion about a function of consciousness. Behavioral and Brain Sciences, 18(2), 227- 287.


Block. (1997). Anti-Reductionism Slaps Back. Noûs, 31(s11), 107-132. doi:https://doi.org/10.1111/0029- 4624.31.s11.5


Blum, L., & Blum, M. (2023). A Theoretical Computer Science Perspective on Consciousness and Artificial General Intelligence. Engineering, 25, 12-16. doi:https://doi.org/10.1016/j.eng.2023.03.010


Boakes, R. (1984). From Darwin to behaviourism. Psychology and the minds of animals. Cambridge, MA: Cambridge University Press.


Boden, M. A. (2016). AI : its nature and future (First edition. ed.). Oxford, United Kingdom: Oxford University Press.


Botvinick, Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement Learning, Fast and Slow. Trends Cogn Sci, 23(5), 408-422. doi:10.1016/j.tics.2019.02.006


Botvinick, Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020). Deep Reinforcement Learning and Its Neuroscientific Implications. Neuron, 107, 603-616.


Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., . . . Eleftheriou, E. (2018). Neuromorphic computing with multi-memristive synapses. Nat Commun, 9(1), 2514. doi:10.1038/s41467-018-04933-y


Buckner, R. L., & Krienen, F. M. (2013). The evolution of distributed association networks in the human brain. Trends Cogn Sci, 17(12), 648-665. doi:10.1016/j.tics.2013.09.017


Burke, S. M., Avstrikova, M., Noviello, C. M., Mukhtasimova, N., Changeux, J.-P., Thakur, G. A., . . . Hibbs, R. E. (2024). Structural mechanisms of α7 nicotinic receptor allosteric modulation and activation. Cell, 187(5), 1160-1176.e1121. doi:https://doi.org/10.1016/j.cell.2024.01.032


Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constant, A., . . . VanRullen, R. (2023). Consciousness in Artificial Intelligence: Insights from the Science of Consciousness. Retrieved from arXiv:2308.08708v3


Cao, R. (2022). Multiple realizability and the spirit of functionalism. Synthese, 200(6), 506. doi:10.1007/s11229-022-03524-1


Castro-Caldas, A., Petersson, K. M., Reis, A., Stone-Elander, S., & Ingvar, M. (1998). The illiterate brain. Learning to read and write during childhood influences the functional organization of the adult brain. Brain, 121 ( Pt 6), 1053-1063. doi:10.1093/brain/121.6.1053


Changeux. (1986). Neuronal man : the biology of mind. New York: Oxford University Press.


Changeux. (1994). Raison et plaisir. Paris: Editions O. Jacob.


Changeux. (2006). The Ferrier Lecture 1998. The molecular biology of consciousness investigated with genetically modified mice. Philos Trans R Soc Lond B Biol Sci, 361(1476), 2239-2259. doi:10.1098/rstb.2006.1832


Changeux. (2017). Climbing Brain Levels of Organisation from Genes to Consciousness. Trends Cogn Sci, 21(3), 168-181. doi:10.1016/j.tics.2017.01.004


Changeux. (2019). 21C1Artistic Creativity: A Neuronal HypothesisSecrets of Creativity: What Neuroscience, the Arts, and Our Minds Reveal (pp. 0): Oxford University Press. Retrieved from https://doi.org/10.1093/oso/9780190462321.003.0002. doi:10.1093/oso/9780190462321.003.0002


Changeux. (2023). Le Beau et la splendeur du vrai: Entretiens avec François L'Yvonnet. Paris: Albin Michel.


Changeux, & Connes, A. (1995). Conversations on mind, matter, and mathematics. Princeton, N.J.: Princeton University Press.


Changeux, Courrège, P., & Danchin, A. (1973). A theory of the epigenesis of neuronal networks by selective stabilization of synapses. Proc Natl Acad Sci U S A, 70(10), 2974-2978.


Changeux, & Danchin, A. (1976). Selective stabilisation of developing synapses as a mechanism for the specification of neuronal networks. Nature, 264(5588), 705-712. doi:10.1038/264705a0


Changeux, Goulas, A., & Hilgetag, C. C. (2021). A Connectomic Hypothesis for the Hominization of the Brain. Cereb Cortex, 31(5), 2425-2449. doi:10.1093/cercor/bhaa365


Changeux, & Lou, H. C. (2011). Emergent pharmacology of conscious experience: new perspectives in substance addiction. FASEB J, 25(7), 2098-2108. doi:10.1096/fj.11-0702ufm


Changeux, & Ricoeur, P. (1998). Ce Qui Nous Fait Penser. La Nature Et La Regle. Paris: Odile Jacob.


Chella, A., Frixione, M., & Gaglio, S. (2008). A cognitive architecture for robot self-consciousness. Artificial Intelligence in Medicine, 44(2), 147-154. doi:https://doi.org/10.1016/j.artmed.2008.07.003


Chella, A., & Manzotti, R. (2009). MACHINE CONSCIOUSNESS: A MANIFESTO FOR ROBOTICS. International Journal of Machine Consciousness, 01(01), 33-51. doi:10.1142/S1793843009000062


Chella, A., Pipitone, A., Morin, A., & Racy, F. (2020). Developing Self-Awareness in Robots via Inner Speech. Front Robot AI, 7. doi:10.3389/frobt.2020.00016


Chirimuuta, M. (2024). The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience: The MIT Press.


Colas, C., Karch, T., Moulin-Frier, C., & Oudeyer, P.-Y. (2022). Language and culture internalization for human-like autotelic AI. Nature Machine Intelligence, 4(12), 1068-1076. doi:10.1038/s42256-022- 00591-4


Cramer, B., Billaudelle, S., Kanya, S., Leibfried, A., Grubl, A., Karasenko, V., . . . Zenke, F. (2022). Surrogate gradients for analog neuromorphic computing. Proc Natl Acad Sci U S A, 119(4). doi:10.1073/pnas.2109194119


Damasio, & Damasio, H. (2022). Homeostatic feelings and the biology of consciousness. Brain, 145(7), 2231- 2235. doi:10.1093/brain/awac194


Damasio, & Damasio, H. (2023). Feelings Are the Source of Consciousness. Neural Comput, 35(3), 277-286. doi:10.1162/neco_a_01521


Dehaene-Lambertz, G., & Spelke, E. S. (2015). The Infancy of the Human Brain. Neuron, 88(1), 93-109. doi:10.1016/j.neuron.2015.09.026


Dehaene, S., & Changeux, J. P. (1989). A simple model of prefrontal cortex function in delayed-response tasks. J Cogn Neurosci, 1(3), 244-261. doi:10.1162/jocn.1989.1.3.244


Dehaene, S., & Changeux, J. P. (1991). The Wisconsin Card Sorting Test: theoretical analysis and modeling in a neuronal network. Cereb Cortex, 1(1), 62-79. doi:10.1093/cercor/1.1.62


Dehaene, S., & Changeux, J. P. (1997). A hierarchical neuronal network for planning behavior. Proc Natl Acad Sci U S A, 94(24), 13293-13298. doi:10.1073/pnas.94.24.13293


Dehaene, S., & Changeux, J. P. (2000). Reward-dependent learning in neuronal networks for planning and decision making. Prog Brain Res, 126, 217-229. doi:10.1016/s0079-6123(00)26016-0


Dehaene, S., & Changeux, J. P. (2005). Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness. PLoS Biol, 3(5), e141. doi:10.1371/journal.pbio.0030141


Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200-227. doi:10.1016/j.neuron.2011.03.018


Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. Proc Natl Acad Sci U S A, 95(24), 14529-14534.


Dehaene, S., Lau, H., & Kouider, S. (2017). What is consciousness, and could machines have it? Science, 358(6362), 486-492. doi:10.1126/science.aan8871


Dehaene, S., Pegado, F., Braga, L. W., Ventura, P., Nunes Filho, G., Jobert, A., . . . Cohen, L. (2010). How learning to read changes the cortical networks for vision and language. Science, 330(6009), 1359- 1364. doi:10.1126/science.1194140


Dehaene, S., Sergent, C., & Changeux, J. P. (2003). A neuronal network model linking subjective reports and objective physiological data during conscious perception. Proc Natl Acad Sci U S A, 100(14), 8520- 8525. doi:10.1073/pnas.1332574100


Del Cul, A., Dehaene, S., Reyes, P., Bravo, E., & Slachevsky, A. (2009). Causal role of prefrontal cortex in the threshold for access to consciousness. Brain, 132(Pt 9), 2531-2540. doi:10.1093/brain/awp111


Deperrois, N., Petrovici, M. A., Senn, W., & Jordan, J. (2022). Learning cortical representations through perturbed and adversarial dreaming. eLife, 11. doi:10.7554/eLife.76384


Deperrois, N., Petrovici, M. A., Senn, W., & Jordan, J. (2024). Learning beyond sensations: How dreams organize neuronal representations. Neurosci Biobehav Rev, 157, 105508. doi:10.1016/j.neubiorev.2023.105508


Dold, D., Bytschok, I., Kungl, A. F., Baumbach, A., Breitwieser, O., Senn, W., . . . Petrovici, M. A. (2019). Stochasticity from function — Why the Bayesian brain may need no noise. Neural Networks, 119, 200-213. doi:https://doi.org/10.1016/j.neunet.2019.08.002


Dromnelle, R., Renaudo, E., Chetouani, M., Maragos, P., Chatila, R., Girard, B., & Khamassi, M. (2023). Reducing Computational Cost During Robot Navigation and Human–Robot Interaction with a Human-Inspired Reinforcement Learning Architecture. International Journal of Social Robotics, 15(8), 1297-1323. doi:10.1007/s12369-022-00942-6


Dubois, J., Kostovic, I., & Judas, M. (2015). Development of structural and functional connectivity.


Dumas, G., Nadel, J., Soussignan, R., Martinerie, J., & Garnero, L. (2010). Inter-brain synchronization during social interaction. PLoS One, 5(8), e12166. doi:10.1371/journal.pone.0012166


Dung, & Newen, A. (2023). Profiles of animal consciousness: A species-sensitive, two-tier account to quality and distribution. Cognition, 235, 105409. doi:10.1016/j.cognition.2023.105409


Dung, L. (2023). Tests of Animal Consciousness are Tests of Machine Consciousness. Erkenntnis. doi:10.1007/s10670-023-00753-9


Edelman. (1992). Bright air, brilliant fire : on the matter of the mind. New York, NY: BasicBooks.


Edelman, & Gally, J. A. (2001). Degeneracy and complexity in biological systems. Proc Natl Acad Sci U S A, 98(24), 13763-13768. doi:10.1073/pnas.231499798


Edelman, & Mountcastle, V. B. (1978). The mindful brain: Cortical organization and the group-selective theory of higher brain function. Oxford, England: Massachusetts Inst of Technology Pr.


Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., & Rasmussen, D. (2012). A large-scale model of the functioning brain. Science, 338(6111), 1202-1205. doi:10.1126/science.1225266


Esser, S. K., Merolla, P. A., Arthur, J. V., Cassidy, A. S., Appuswamy, R., Andreopoulos, A., . . . Modha, D. S. (2016). Convolutional networks for fast, energy-efficient neuromorphic computing. Proc Natl Acad Sci U S A, 113(41), 11441-11446. doi:10.1073/pnas.1604850113


Evers. (2009). Neuroetique. Quand la matière s'éveille. Paris: Odile Jacob.


Evers. (2015). Can we be epigenetically proactive? In W. Metzinger T., J. (Ed.), Open Mind: Philosophy and the mind sciences in the 21st century. Cambridge, MA: MIT Press.


Evers, & Changeux, J. P. (2016). Proactive epigenesis and ethical innovation: A neuronal hypothesis for the genesis of ethical rules. EMBO Rep, 17(10), 1361-1364. doi:10.15252/embr.201642783


Evers, & Sigman, M. (2013). Possibilities and limits of mind-reading: A neurophilosophical perspective. Consciousness and Cognition, 22, 887-897.


Farisco, M. (2024). The ethical implications of indicators of consciousness in artificial systems Developments in Neuroethics and Bioethics: Academic Press.


Farisco, M., Baldassarre, G., Cartoni, E., Leach, A., Petrovici, M. A., Rosemann, A., . . . Van Albada, S. J. (2023). A method for the ethical analysis of brain-inspired AI. Retrieved from arXiv:2305.10938v1 website:


Farisco, M., Kotaleski, J. H., & Evers, K. (2018). Large-Scale Brain Simulation and Disorders of Consciousness. Mapping Technical and Conceptual Issues. Front Psychol, 9, 585. doi:10.3389/fpsyg.2018.00585


Farisco, M., Laureys, S., & Evers, K. (2015). Externalization of consciousness. Scientific possibilities and clinical implications. Curr Top Behav Neurosci, 19, 205-222. doi:10.1007/7854_2014_338


Floreano, D., Ijspeert, A. J., & Schaal, S. (2014). Robotics and neuroscience. Curr Biol, 24(18), R910-R920. doi:10.1016/j.cub.2014.07.058


Floreano, D., & Mattiussi, C. (2008). Bio-Inspired Artificial Intelligence. Cambridge, MA: MIT Press.


Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O⿿Doherty, J., & Pezzulo, G. (2016). Active inference and learning. Neuroscience & Biobehavioral Reviews, 68, 862-879. doi:https://doi.org/10.1016/j.neubiorev.2016.06.022


Godfrey-Smith, P. (2023). Nervous Systems, Functionalism, and Artificial Minds. Retrieved from https://petergodfreysmith.com/wp-content/uploads/2023/12/NYU-Oct-2023-Animals-AIFunctionalism-paper-Post-C3.pdf


Göltz, J., Kriener, L., Sabado, V., & Petrovici, M. A. (2021). Fast and Energy-efficient Deep Neuromorphic Learning. ERCIM NEWS, 125, 17-18.


Grillner, S., Deliagina, T., Ekeberg, O., el Manira, A., Hill, R. H., Lansner, A., . . . Wallén, P. (1995). Neural networks that co-ordinate locomotion and body orientation in lamprey. Trends Neurosci, 18(6), 270-279.


Grondin, S. (2001). From physical time to the first and second moments of psychological time. Psychol Bull, 127(1), 22-44. doi:10.1037/0033-2909.127.1.22


Grover, D., Chen, J. Y., Xie, J., Li, J., Changeux, J. P., & Greenspan, R. J. (2022). Differential mechanisms underlie trace and delay conditioning in Drosophila. Nature, 603(7900), 302-308. doi:10.1038/s41586-022-04433-6


Guerguiev, J., Lillicrap, T. P., & Richards, B. A. (2017). Towards deep learning with segregated dendrites. eLife, 6. doi:10.7554/eLife.22901


Gupta, A., Savarese, S., Ganguli, S., & Fei-Fei, L. (2021). Embodied intelligence via learning and evolution. Nature Communications, 12(1), 5721. doi:10.1038/s41467-021-25874-z


Haider, P., Ellenberger, B., Kriener, L., Jordan, J., Senn, W., & Petrovici, M. A. (2021). Latent equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons. Advances in Neural Information Processing Systems, 34, 17839-17851.


Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-Inspired Artificial Intelligence. Neuron, 95(2), 245-258. doi:10.1016/j.neuron.2017.06.011


Hildt, E. (2023). The Prospects of Artificial Consciousness: Ethical Dimensions and Concerns. AJOB Neurosci, 14(2), 58-71. doi:10.1080/21507740.2022.2148773


Hoel, E. P. (2017). When the Map Is Better Than the Territory. Entropy, 19(5), 188.


Hopster, J., & Löhr, G. (2023). Conceptual Engineering and Philosophy of Technology: Amelioration or Adaptation? Philosophy & Technology, 36(4), 70. doi:10.1007/s13347-023-00670-3


Hublin, J.-J., & Changeux, J. P. (2022). Paleoanthropology of cognition: an overview on Hominins brain evolution. Comptes Rendus Biologies, 345(2), 57-75.


Humphries, M. D., Khamassi, M., & Gurney, K. (2012). Dopaminergic Control of the Exploration-Exploitation Trade-Off via the Basal Ganglia. Front Neurosci, 6, 9. doi:10.3389/fnins.2012.00009


Irwin, L. N. (2024). Behavioral indicators of heterogeneous subjective experience in animals across the phylogenetic spectrum: Implications for comparative animal phenomenology. Heliyon. doi:10.1016/j.heliyon.2024.e28421


Jékely, G. (2021). The chemical brain hypothesis for the origin of nervous systems. Philos Trans R Soc Lond B Biol Sci, 376(1821), 20190761. doi:10.1098/rstb.2019.0761


Jordan, J., Schmidt, M., Senn, W., & Petrovici, M. A. (2021). Evolving interpretable plasticity for spiking networks. eLife, 10, e66273. doi:10.7554/eLife.66273


Kanaev, I. A. (2022). Evolutionary origin and the development of consciousness. Neuroscience & Biobehavioral Reviews, 133, 104511. doi:https://doi.org/10.1016/j.neubiorev.2021.12.034


Kasthuri, N., & Lichtman, J. W. (2003). The role of neuronal identity in synaptic competition. Nature, 424(6947), 426-430. doi:10.1038/nature01836


Kelty-Stephen, D., Cisek, P. E., De Bari, B., Dixon, J., Favela, L. H., Hasselman, F., . . . Mangalam, M. (2022). In search for an alternative to the computer metaphor of the mind and brain. Retrieved from arXiv:2206.04603


Klatzmann, U., Froudist-Walsh, S., Bliss, D., Theodoni, P., Mejías, J., Niu, M., . . . Wang, X.-J. (2023). A connectome-based model of conscious access in monkey cortex. bioRxiv, 2022.2002.2020.481230. doi:10.1101/2022.02.20.481230


Kleiner, J. (2024). Consciousness qua Mortal Computation.


Kouider, S., Stahlhut, C., Gelskov, S. V., Barbosa, L. S., Dutat, M., de Gardelle, V., . . . Dehaene-Lambertz, G. (2013). A neural marker of perceptual consciousness in infants. Science, 340(6130), 376-380. doi:10.1126/science.1232509


Koukouli, F., Rooy, M., Changeux, J. P., & Maskos, U. (2016). Nicotinic receptors in mouse prefrontal cortex modulate ultraslow fluctuations related to conscious processing. Proc Natl Acad Sci U S A, 113(51), 14823-14828. doi:10.1073/pnas.1614417113


Lagercrantz. (2016). Infant Brain Development : Formation of the Mind and the Emergence of Consciousness (pp. 1 online resource (XI, 156 pages 195 illustrations, 170 illustrations in color). doi:10.1007/978-3- 319-44845-9


Lagercrantz, & Changeux, J. P. (2009). The emergence of human consciousness: from fetal to neonatal life. Pediatr Res, 65(3), 255-260. doi:10.1203/PDR.0b013e3181973b0d


Lagercrantz, & Changeux, J. P. (2010). Basic consciousness of the newborn. Semin Perinatol, 34(3), 201-206. doi:10.1053/j.semperi.2010.02.004


LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539


LeDoux, Birch, J., Andrews, K., Clayton, N. S., Daw, N. D., Frith, C., . . . Vandekerckhove, M. M. P. (2023). Consciousness beyond the human case. Current Biology, 33(16), R832-R840. doi:https://doi.org/10.1016/j.cub.2023.06.067


Lenharo, M. (2024). AI consciousness: scientists say we urgently need answers. Nature, 625(7994), 226. doi:10.1038/d41586-023-04047-6


Levine, J. (1983). Materialism and qualia: the explanatory gap. Pacific Philosophical Quarterly, 64, 354-361.


Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L., & Corbetta, M. (2009). Learning sculpts the spontaneous activity of the resting human brain. Proceedings of the National Academy of Sciences, 106(41), 17558-17563. doi:doi:10.1073/pnas.0902455106


Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020). Backpropagation and the brain. Nat Rev Neurosci, 21(6), 335-346. doi:10.1038/s41583-020-0277-3


Lou, H. C., Changeux, J. P., & Rosenstand, A. (2016). Towards a cognitive neuroscience of self-awareness. Neurosci Biobehav Rev. doi:10.1016/j.neubiorev.2016.04.004


Lou, H. C., Changeux, J. P., & Rosenstand, A. (2017). Towards a cognitive neuroscience of self-awareness. Neurosci Biobehav Rev, 83, 765-773. doi:10.1016/j.neubiorev.2016.04.004


Man, K., Damásio, A. S., & Neven, H. (2022). Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift. ArXiv, abs/2205.08645.


Marcus, G., & Davis, E. (2019). Rebooting AI : building artificial intelligence we can trust (First edition. ed.). New York: Pantheon Books.


Mashour, Roelfsema, Changeux, & Dehaene. (2020a). Conscious Processing and the Global Neuronal Workspace Hypothesis. Neuron, 105(5), 776-798. doi:10.1016/j.neuron.2020.01.026


Mashour, Roelfsema, P., Changeux, J.-P., & Dehaene, S. (2020b). Conscious processing and the global neuronal workspace hypothesis. Neuron, 105(5), 776-798.


Max, K., Kriener, L., Pineda García, G., Nowotny, T., Senn, W., & Petrovici, M. A. (2023, 2023//). Learning Efficient Backprojections Across Cortical Hierarchies in Real Time. Paper presented at the Artificial Neural Networks and Machine Learning – ICANN 2023, Cham.


McCulloch, W., & Pitts, W. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 4(4), 115-133.


Melis, A. P., & Raihani, N. J. (2023). The cognitive challenges of cooperation in human and nonhuman animals. Nature Reviews Psychology, 2(9), 523-536. doi:10.1038/s44159-023-00207-7


Mesoudi, A., Laland, K. N., Boyd, R., Buchanan, B., Flynn, E., McCauley, R. N., . . . Tennie, C. (2013). 193The Cultural Evolution of Technology and Science. In P. J. Richerson & M. H. Christiansen (Eds.), Cultural Evolution: Society, Technology, Language, and Religion (pp. 0): The MIT Press.


Metzinger, T. (2021). An Argument for a Global Moratorium onSynthetic Phenomenology. Journal of Arti¯cial Intelligence and Consciousness, 8(1), 1-24.


Miglino, O., Lund, H. H., & Nolfi, S. (1995). Evolving mobile robots in simulated and real environments. Artif Life, 2(4), 417-434. doi:10.1162/artl.1995.2.4.417


Minsky, M., & Papert, S. A. (2017). Perceptrons: An Introduction to Computational Geometry: The MIT Press.


Mitchell, M. (2023). How do we know how smart AI systems are? Science, 381(6654), adj5957. doi:10.1126/science.adj5957


Mitchell, M., & Krakauer, D. C. (2023). The debate over understanding in AI's large language models. Proc Natl Acad Sci U S A, 120(13), e2215907120. doi:10.1073/pnas.2215907120


Momennejad, I. (2023). A rubric for human-like agents and NeuroAI. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1869), 20210446. doi:doi:10.1098/rstb.2021.0446


Montemayor, C. (2023). The Prospect of a Humanitarian Artificial Intelligence : agency and value alignment.


Moulin-Frier, C., Arsiwalla, X. D., Puigbo, J.-Y., Sánchez-Fibla, M., Duff, A., & Verschure, P. F. M. J. (2016). Top-Down and Bottom-Up Interactions between Low-Level Reactive Control and Symbolic Rule Learning in Embodied Agents. https://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper8.pdf


Moutard, C., Dehaene, S., & Malach, R. (2015). Spontaneous Fluctuations and Non-linear Ignitions: Two Dynamic Faces of Cortical Recurrent Loops. Neuron, 88(1), 194-206. doi:10.1016/j.neuron.2015.09.018


Nolfi, S., Parisi, D., & Elman, J. L. (1994). Learning and Evolution in Neural Networks. Adaptive Behavior, 3(1), 5-28. doi:10.1177/105971239400300102


Oliveira, A. L. (2022). A blueprint for conscious machines. Proceedings of the National Academy of Sciences, 119(23), e2205971119. doi:10.1073/pnas.2205971119


Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54-71. doi:https://doi.org/10.1016/j.neunet.2019.01.012


Park, Lee, J., & Jeon, D. (2019, 17-21 Feb. 2019). 7.6 A 65nm 236.5nJ/Classification Neuromorphic Processor with 7.5% Energy Overhead On-Chip Learning Using Direct Spike-Only Feedback. Paper presented at the 2019 IEEE International Solid- State Circuits Conference - (ISSCC).


Pennartz. (2009). Identification and integration of sensory modalities: neural basis and relation to consciousness. Conscious Cogn, 18(3), 718-739. doi:10.1016/j.concog.2009.03.003


Pennartz. (2024). The consciousness network : how the brain creates our reality. Abingdon, Oxon ; New York, NY: Routledge.


Pennartz, Farisco, M., & Evers, K. (2019). Indicators and Criteria of Consciousness in Animals and Intelligent Machines: An Inside-Out Approach. Front Syst Neurosci, 13, 25. doi:10.3389/fnsys.2019.00025


Petrovici, Bill, J., Bytschok, I., Schemmel, J., & Meier, K. (2016). Stochastic inference with spiking neurons in the high-conductance state. Physical Review E, 94(4), 042312. doi:10.1103/PhysRevE.94.042312


Petrovici, Vogginger, B., Muller, P., Breitwieser, O., Lundqvist, M., Muller, L., . . . Meier, K. (2014). Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms. PLoS One, 9(10), e108590. doi:10.1371/journal.pone.0108590


Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2024). Generating meaning: active inference and the scope and limits of passive AI. Trends Cogn Sci, 28(2), 97-112. doi:10.1016/j.tics.2023.10.002


Pfeil, T., Grübl, A., Jeltsch, S., Müller, E., Müller, P., Petrovici, M. A., . . . Meier, K. (2013). Six networks on a universal neuromorphic computing substrate. Front Neurosci, 7, 11. doi:10.3389/fnins.2013.00011


Phillips, W. A. (2023). The cooperative neuron. New York: Oxford University Press.


Piccinini, G. (2022). Situated Neural Representations: Solving the Problems of Content. Front Neurorobot, 16, 846979. doi:10.3389/fnbot.2022.846979


Pipitone, A., & Chella, A. (2021). Robot passes the mirror test by inner speech. Robotics and Autonomous Systems, 144, 103838. doi:https://doi.org/10.1016/j.robot.2021.103838


Poo, M.-m. (2018). Towards brain-inspired artificial intelligence. National Science Review, 5(6), 785-785. doi:10.1093/nsr/nwy120


Posner, M. I., & Rothbart, M. K. (2007). Educating the human brain. Washington, DC, US: American Psychological Association.


Raiteri, M. (2006). Functional pharmacology in human brain. Pharmacol Rev, 58(2), 162-193. doi:10.1124/pr.58.2.5


Rochat, P. (2003). Five levels of self-awareness as they unfold early in life. Consciousness and Cognition: An International Journal, 12(4), 717-731. doi:10.1016/S1053-8100(03)00081-3


Roli, Jaeger, J., & Kauffman, S. A. (2022). How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence. Frontiers in Ecology and Evolution, 9. doi:10.3389/fevo.2021.806283


Rosas, F. E., Mediano, P. A. M., Jensen, H. J., Seth, A. K., Barrett, A. B., Carhart-Harris, R. L., & Bor, D. (2020). Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data. PLoS Comput Biol, 16(12), e1008289. doi:10.1371/journal.pcbi.1008289


Sandved-Smith, L., Hesp, C., Mattout, J., Friston, K., Lutz, A., & Ramstead, M. J. D. (2021). Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference. Neuroscience of Consciousness, 2021(1). doi:10.1093/nc/niab018


Scellier, B., & Bengio, Y. (2017). Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation. Front Comput Neurosci, 11, 24. doi:10.3389/fncom.2017.00024


Schurger, A., Kim, M.-S., & Cohen, J. D. (2015). Paradoxical interaction between ocular activity, perception, and decision confidence at the threshold of vision. PLoS One, 10(5). doi:10.1371/journal.pone.0125278


Searle, J. R. (2000). Consciousness. Annu Rev Neurosci, 23, 557-578. doi:10.1146/annurev.neuro.23.1.557


Self, M. W., Kooijmans, R. N., Supèr, H., Lamme, V. A., & Roelfsema, P. R. (2012). Different glutamate receptors convey feedforward and recurrent processing in macaque V1. Proc Natl Acad Sci U S A, 109(27), 11031-11036. doi:10.1073/pnas.1119527109


Senn, W., Dold, D., Kungl, A. F., Ellenberger, B., Jordan, J., Bengio, Y., . . . Petrovici, M. A. (2023). A Neuronal Least-Action Principle for Real-Time Learning in Cortical Circuits. bioRxiv, 2023.2003.2025.534198. doi:10.1101/2023.03.25.534198


Seth. (2021). Being you the inside story of your inner universe (pp. 1 online resource). Retrieved from http://link.overdrive.com/?websiteID=110056&titleID=5068666


Seth. (2024). Conscious artificial intelligence and biological naturalism. Retrieved from https://doi.org/10.31234/osf.io/tz6an website:


Shanahan. (2024). Talking about Large Language Models. Commun. ACM, 67(2), 68–79. doi:10.1145/3624724


Shanahan, Crosby, M., Beyret, B., & Cheke, L. (2021). Artificial Intelligence and the Common Sense of Animals: (Trends in Cognitive Sciences 24, 862-872, 2020). Trends Cogn Sci, 25(2), 172. doi:10.1016/j.tics.2020.10.008


Shapiro, L. A., & Spaulding, S. (2024). The Routledge handbook of embodied cognition (Second edition. ed.). Abingdon, Oxon ; New York, NY: Routledge.


Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., . . . Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140-1144. doi:10.1126/science.aar6404


Smaldino, P. E. (2017). Models Are Stupid, and We Need More of Them.


Solée, R. V., Valverde, S., Casals, M. R., Kauffman, S. A., Farmer, D., & Eldredge, N. (2013). The evolutionary ecology of technological innovations. Complexity, 18(4), 15-27. doi:https://doi.org/10.1002/cplx.21436


Stent, G. S., Kristan, W. B., Jr., Friesen, W. O., Ort, C. A., Poon, M., & Calabrese, R. L. (1978). Neuronal generation of the leech swimming movement. Science, 200(4348), 1348-1357. doi:10.1126/science.663615


Thompson, E. (2018). Biopsychism, Minimal Life, and Sentience. Retrieved from https://psa2018.philsci.org/user-profile/abstract/public/352/biopsychism-minimal-life-andsentience


Tomasello, M. (2022). The evolution of agency : behavioral organization from lizards to humans. Cambridge, Massachusetts: The MIT Press.


Tononi. (2015). Integrated information theory Scholapedia (Vol. 10 (1)).


Tononi, & Edelman, G. M. (1998). Consciousness and complexity. Science, 282(5395), 1846-1851. doi:10.1126/science.282.5395.1846


Tononi, G., Sporns, O., & Edelman, G. M. (1999). Measures of degeneracy and redundancy in biological networks. Proceedings of the National Academy of Sciences, 96(6), 3257-3262. doi:doi:10.1073/pnas.96.6.3257


Ugur, B., Chen, K., & Bellen, H. J. (2016). Drosophila tools and assays for the study of human diseases. Dis Model Mech, 9(3), 235-244. doi:10.1242/dmm.023762


Valverde, S. (2016). Major transitions in information technology. Philos Trans R Soc Lond B Biol Sci, 371(1701). doi:10.1098/rstb.2015.0450


van Rooij, I., Guest, O., Adolfi, F., de Haan, R., Kolokolova, A., & Rich, P. (2023). Reclaiming AI as a theoretical tool for cognitive science. Retrieved from https://osf.io/preprints/psyarxiv/4cbuv website:


VanRullen, R., & Kanai, R. (2021). Deep learning and the Global Workspace Theory. Trends in Neurosciences, 44(9), 692-704. doi:https://doi.org/10.1016/j.tins.2021.04.005


Varela, F. J., Thompson, E., & Rosch, E. (2016). The embodied mind : cognitive science and human experience (revised edition. ed.). Cambridge, Massachusetts ; London England: MIT Press.


Verschure, P. F. (2016). Synthetic consciousness: the distributed adaptive control perspective. Philos Trans R Soc Lond B Biol Sci, 371(1701). doi:10.1098/rstb.2015.0448


Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., . . . Silver, D. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350- 354. doi:10.1038/s41586-019-1724-z


Volzhenin, K., Changeux, J. P., & Dumas, G. (2022). Multilevel development of cognitive abilities in an artificial neural network. Proc Natl Acad Sci U S A, 119(39), e2201304119. doi:10.1073/pnas.2201304119


Waldrop, M. M. (2019). What are the limits of deep learning? Proceedings of the National Academy of Sciences, 116(4), 1074-1077. doi:doi:10.1073/pnas.1821594116


Walter, J. (2021). Consciousness as a multidimensional phenomenon: implications for the assessment of disorders of consciousness. Neurosci Conscious, 2021(2), niab047. doi:10.1093/nc/niab047


Wang. (1999). Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J Neurosci, 19(21), 9587-9603. doi:10.1523/jneurosci.19-21-09587.1999


Wang, Agrawal, A., Yu, E., & Roy, K. (2021). Multi-Level Neuromorphic Devices Built on Emerging Ferroic Materials: A Review. Front Neurosci, 15, 661667. doi:10.3389/fnins.2021.661667


Wang, Yang, Y., Wang, C. J., Gamo, N. J., Jin, L. E., Mazer, J. A., . . . Arnsten, A. F. (2013). NMDA receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex. Neuron, 77(4), 736-749. doi:10.1016/j.neuron.2012.12.032


Whittington, J. C. R., & Bogacz, R. (2017). An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity. Neural Comput, 29(5), 1229- 1262. doi:10.1162/NECO_a_00949


Wingo, A. P., Dammer, E. B., Breen, M. S., Logsdon, B. A., Duong, D. M., Troncosco, J. C., . . . Wingo, T. S. (2019). Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age. Nat Commun, 10(1), 1619. doi:10.1038/s41467-019-09613-z


Wolfram, S. (2023). What Is ChatGPT Doing ... and Why Does It Work? Retrieved from writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work website:


Yang, C., Kobayashi, S., Nakao, K., Dong, C., Han, M., Qu, Y., . . . Hashimoto, K. (2018). AMPA Receptor Activation-Independent Antidepressant Actions of Ketamine Metabolite (S)-Norketamine. Biol Psychiatry, 84(8), 591-600. doi:10.1016/j.biopsych.2018.05.007


Zador, A., Escola, S., Richards, B., Olveczky, B., Bengio, Y., Boahen, K., . . . Tsao, D. (2023). Catalyzing nextgeneration Artificial Intelligence through NeuroAI. Nat Commun, 14(1), 1597. doi:10.1038/s41467- 023-37180-x


Zelazo, P. D., Craik, F. I., & Booth, L. (2004). Executive function across the life span. Acta Psychol (Amst), 115(2-3), 167-183. doi:10.1016/j.actpsy.2003.12.005


Zhang, C., Chen, J., Li, J., Peng, Y., & Mao, Z. (2023). Large language models for human–robot interaction: A review. Biomimetic Intelligence and Robotics, 3(4), 100131. doi:https://doi.org/10.1016/j.birob.2023.100131


Authors:

(1) Michele Farisco, Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden and Biogem, Biology and Molecular Genetics Institute, Ariano Irpino (AV), Italy;

(2) Kathinka Evers, Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden;

(3) Jean-Pierre Changeux, Neuroscience Department, Institut Pasteur and Collège de France Paris, France.


This paper is available on arxiv under CC BY 4.0 DEED license.


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