Extents and ways in which AI has been inspired by understanding of the brain
Embodiment of conscious processing: hierarchy and parallelism of nested levels of organization
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
Conscious vs non-conscious processing in the brain, or res cogitans vs res extensa
AI consciousness and social interaction challenge rational thinking and language
Conclusion, Acknowledgments, and References
ConclusionWe 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.
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\ 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.
ReferencesAlbantakis, 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/necoa01521
\ 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:10.1007/78542014338
\ 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., ODoherty, 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/CoCoNIPS2016paper8.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/NECOa00949
\ 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
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:::info 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.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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