The Neurobiology of Cognition in the Age of Artificial Intelligence: From Synaptic Plasticity to Cognitive Mapping

Authors

  • Taruna Ikrar Indonesia FDA, Jl. Percetakan Negara, No.23, Jakarta Pusat, 10560, Indonesia
  • Alfi Sophian Indonesia FDA, Jl. Percetakan Negara, No.23, Jakarta Pusat, 10560, Indonesia 0000-0002-5206-2110
10.5281/zenodo.17938553

Abstract

Cognition emerges from complex interactions among molecular, cellular, and network-level processes in the brain that allow adaptive representation, reasoning, and behavioral regulation. Recent advances in neurobiology, combined with artificial intelligence (AI) and computational modeling, have illuminated previously inaccessible aspects of cognitive mechanisms—ranging from synaptic plasticity to large-scale cognitive mapping. This review explores how neural substrates underpin core cognitive processes such as attention, memory, and prediction, and how these biological architectures inspire AI systems through neuro-symbolic and neuromorphic approaches. We examine the dynamic relationship between predictive coding, hierarchical cortical networks, and consciousness as a distributed emergent property. Furthermore, new insights from connectomics, optogenetics, and neural decoding provide unprecedented clarity about the biophysical basis of cognition. The convergence between neurobiology and AI offers not only models of intelligence but also novel frameworks to understand self-referential cognition, agency, and ethical implications of synthetic minds. Ultimately, the neurobiology of cognition is entering a transformative era where understanding the brain’s logic informs both human enhancement and machine consciousness.

Keywords:

cognition, neurobiology, predictive coding, neuro-AI convergence, synaptic plasticity

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References

Alink A, Schwiedrzik CM, Kohler A, Singer W, Muckli L. Stimulus predictability reduces responses in primary visual cortex. J Neurosci. 2010;30(8):2960-2966.

Araque A, Carmignoto G, Haydon PG, Oliet SHR, Robitaille R, Volterra A. Gliotransmitters travel in time and space. Neuron. 2014;81(4):728-739.

Aston-Jones G, Cohen JD. An integrative theory of locus coeruleus–norepinephrine function: adaptive gain and optimal performance. Annu Rev Neurosci. 2005;28:403-450.

Autry AE, Monteggia LM. Brain-derived neurotrophic factor and neuropsychiatric disorders. Pharmacol Rev. 2012;64(2):238-258.

Azevedo FAC, Carvalho LRB, Grinberg LT, et al. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comp Neurol. 2009;513(5):532-541.

Bassett DS, Sporns O. Network neuroscience. Nat Neurosci. 2017;20(3):353-364.

Bassett DS, Yang M, Wymbs NF, Grafton ST. Learning-induced autonomy of sensorimotor systems. Nat Neurosci. 2015;18(5):744-751.

Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ. Canonical microcircuits for predictive coding. Neuron. 2012;76(4):695-711.

Bliss TVP, Collingridge GL. A synaptic model of memory: long-term potentiation in the hippocampus. Nature. 1993;361:31-39.

Bellec G, Scherr F, Subramoney A, et al. A solution to the learning dilemma for recurrent networks of spiking neurons. Nat Commun. 2020;11(1):3625.

Binder JR, Desai RH. The neurobiology of semantic memory. Trends Cogn Sci. 2011;15(11):527-536.

Botvinick M, Wang JX, Dabney W, Miller KJ, Kurth-Nelson Z. Deep reinforcement learning and its neuroscientific implications. Neuron. 2020;107(4):603-616.

Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10(3):186-198.

Buschman TJ, Miller EK. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science. 2007;315(5820):1860-1862.

Buzsáki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004;304(5679):1926-1929.

Canolty RT, Knight RT. The functional role of cross-frequency coupling. Trends Cogn Sci. 2010;14(11):506-515.

Cardin JA, Carlén M, Meletis K, et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature. 2009;459(7247):663-667.

Churchland PM. A Neurocomputational Perspective: The Nature of Mind and the Structure of Science. MIT Press; 1989.

Churchland PM. Philosophy at Work. Cambridge University Press; 2007.

Churchland PS. Neurophilosophy: Toward a Unified Science of the Mind-Brain. MIT Press; 1986.

Clark A. An embodied cognitive science? Trends Cogn Sci. 1999;3(9):345-351.

Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav Brain Sci. 2013;36(3):181-204.

Clark A, Chalmers D. The extended mind. Analysis. 1998;58(1):7-19.

Collingridge GL, Peineau S, Howland JG, Wang YT. Long-term depression in the CNS. Nat Rev Neurosci. 2010;11(7):459-473.

Cunningham JP, Yu BM. Dimensionality reduction for large-scale neural recordings. Nat Neurosci. 2014;17(11):1500-1509.

Damasio AR. Descartes’ Error: Emotion, Reason, and the Human Brain. Putnam; 1994.

Davies M, Wild A, Orchard G, Galluppi F. Advancing neuromorphic computing with Loihi: a survey of results and outlook. Proc IEEE. 2021;109(5):911-934.

Day JJ, Sweatt JD. Epigenetic mechanisms in cognition. Neuron. 2011;70(5):813-829.

Deco G, Tononi G, Boly M, Kringelbach ML. Rethinking segregation and integration: contributions of whole-brain modelling. Nat Rev Neurosci. 2015;16(7):430-439.

Deisseroth K. Optogenetics: 10 years of microbial opsins in neuroscience. Nat Neurosci. 2015;18(9):1213-1225.

Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A. Neuroplasticity: changes in grey matter induced by training. Nature. 2004;427(6972):311-312.

Edison P, Holland J, Minhas DS. Neuroimaging biomarkers of cognition and dementia. Nat Rev Neurol. 2022;18(10):603-618.

Eliasmith C, Stewart TC, Choo X, et al. A large-scale model of the functioning brain. Science. 2012;338(6111):1202-1205.

Feldman H, Friston KJ. Attention, uncertainty, and free-energy. Front Hum Neurosci. 2010;4:215.

Fields RD, Woo DH, Basser PJ. Glial regulation of the neuronal connectome through local and long-distant signaling. Neuron. 2015;86(2):374-386.

Floridi L, Sanders JW. On the morality of artificial agents. Minds Mach. 2004;14(3):349-379.

Florio M, Albert M, Taverna E, et al. Human-specific gene ARHGAP11B promotes basal progenitor amplification and neocortex expansion. Science. 2015;347(6229):1465-1470.

Fodor JA. The Language of Thought. Harvard University Press; 1975.

Fries P. Rhythms for cognition: communication through coherence. Neuron. 2015;88(1):220-235.

Friston K. The free-energy principle: a unified brain theory? Nat Rev Neurosci. 2010;11(2):127-138.

Friston KJ, Preller KH, Mathys C, et al. Dynamic causal modelling revisited. Neuroimage. 2019;199:730-744.

Frith CD, Frith U. Mechanisms of social cognition. Annu Rev Psychol. 2012;63:287-313.

Gazzaniga MS, Ivry RB, Mangun GR. Cognitive Neuroscience: The Biology of the Mind. 5th ed. W.W. Norton; 2019.

Greene JD, Sommerville RB, Nystrom LE, Darley JM, Cohen JD. An fMRI investigation of emotional engagement in moral judgment. Science. 2001;293(5537):2105-2108.

Hassabis D, Kumaran D, Summerfield C, Botvinick M. Neuroscience-inspired artificial intelligence. Neuron. 2023;111(5):646-664.

Kandel ER. Principles of Neural Science. 5th ed. McGraw-Hill; 2013.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-444.

Libet B. Unconscious cerebral initiative and the role of conscious will in voluntary action. Behav Brain Sci. 1985;8(4):529-566.

Lisman JE, Jensen O. The theta–gamma neural code. Neuron. 2013;77(6):1002-1016.

Malenka RC, Bear MF. LTP and LTD: an embarrassment of riches. Neuron. 2004;44(1):5-21.

Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15(10):483-506.

Merleau-Ponty M. Phenomenology of Perception. Routledge; 1962.

Newell A, Simon HA. Computer science as empirical inquiry: symbols and search. Commun ACM. 1976;19(3):113-126.

O’Keefe J, Nadel L. The Hippocampus as a Cognitive Map. Oxford University Press; 1978.

Rao RPN, Ballard DH. Predictive coding in the visual cortex. Nat Neurosci. 1999;2(1):79-87.

Schultz W, Dayan P, Montague PR. A neural substrate of prediction and reward. Science. 1997;275(5306):1593-1599.

Searle JR. Minds, brains, and programs. Behav Brain Sci. 1980;3(3):417-457.

Singer W. Neuronal synchrony: a versatile code for the definition of relations? Neuron. 1999;24(1):49-65.

Sporns O. Networks of the Brain. MIT Press; 2018.

Tononi G. An information integration theory of consciousness. BMC Neurosci. 2004;5:42.

Varela FJ, Thompson E, Rosch E. The Embodied Mind: Cognitive Science and Human Experience. MIT Press; 1991.

Wang XJ. Macroscopic gradients of synaptic excitation and inhibition in the neocortex. Nat Rev Neurosci. 2020;21(3):169-178.

Whittington JCR, Bogacz R. Theories of error back-propagation in the brain. Trends Cogn Sci. 2019;23(3):235-250.

Yuste R, Goering S, Arcas BAY, et al. Four ethical priorities for neurotechnologies and AI. Nature. 2017;551(7679):159-163.

Zeisel A, Hochgerner H, Lönnerberg P, et al. Molecular architecture of the mouse nervous system. Cell. 2018;174(4):999-1014.

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Published

11.12.2025

How to Cite

Ikrar, T., & Sophian, A. (2025). The Neurobiology of Cognition in the Age of Artificial Intelligence: From Synaptic Plasticity to Cognitive Mapping. Journal of NeuroPhilosophy, 4(2). https://doi.org/10.5281/zenodo.17938553

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Vol. 4 No. 2 (2025)

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Review Articles

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