Abstract
The aim of this article is a critical analysis of the logocentric representation of brain as a conceptual basis for artificial neural networks (ANNs). Neuro-logocentrism turns possible mostly because of some similarities of brain activity to logical reasoning. They are discrete mode of action, semblance of realization of logical connectives and inferences, presence of two “values” (“all-or-none” principle). It is demonstrated that they are only loose analogies. The idea that neurons are net nodes that realize logical and computational operations as quite simple automata is based on the concept of connectionism that appears a new special type of hylemorphism. On the contrary, brain activity demonstrates the primacy of the whole and its irreducibility to the aggregative sum of elements and parts. It appears continual totality, where everything is reciprocally interconnected, so that it is impossible to establish any unified formalism for it. Brain essentially is a part of the living, not “bio-logic” but fully biological reality, which has an emergent mode of existence with its unique properties. Such processes as neuroplasticity and synaptic pruning are responsible for adaptive and teleonomic re‑shaping of the brain. Usual conventional logic is suitable for computing machines and their nets, but it is incapable of reproducing such autopoietic phenomena. As a result, there is a fatal ontological gap between natural networks of neurons and ANNs. The second ones are not able to implement complex cognitive functions as they are product of the logocentric hypertrophy of algorithms which is fully devoid of flexibility of the living brain.
References
Abraham, T. H. (2002). (Physio)logical circuits: The intellectual origins of the McCulloch–Pitts neural networks. Journal of the History of the Behavioral Sciences, 38(1), 3–25. https://doi.org/10.1002/jhbs.1094
Arendt, D. (2020). The evolutionary assembly of neuronal machinery. Current Biology, 30(10), R603–R616. https://doi.org/10.1016/j.cub.2020.04.008
Bennett, M. R., Dennett, D., Hacker, P., & Searle, J. (2007). Neuroscience and philosophy: Brain, mind, and language. Columbia University Press.
Carter, M. (2007). Minds and computers: An introduction to the philosophy of artificial intelligence. Edinburgh University Press. https://doi.org/10.1515/9780748629305
Churchland, P. S., & Sejnowski, T. J. (2017). The computational brain. MIT Press. https://doi.org/10.7551/mitpress/9780262533393.001.0001
Damasio, A. R. (2010). Self comes to mind. Constructing the Conscious Brain. Vintage.
Deutsch, D., Ekert, A., & Lupacchini, R. (2000). Machines, logic and quantum physics. Bulletin of Symbolic Logic, 6(3), 265–283. https://doi.org/10.2307/421056
Edelman, G. M. (1987). Neural Darwinism: The theory of neuronal group selection. Basic books.
Edelman, G. M., & Tononi, G. (2008). A universe of consciousness: How matter becomes imagination. Basic Books.
Goldblum, N. (2001). The brain-shaped mind: What the brain can tell us about the mind. Cambridge University Press. https://doi.org/10.1017/CBO9780511612749
Groote, J. F. & et al. (2021). Logic Gates, Circuits, Processors, Compilers and Computers. Springer. https://doi.org/10.1007/978-3-030-68553-9
Kamsma, T. M., Kim, J., Kim, K., Boon, W. Q., Spitoni, C., Park, J., & Van Roij, R. (2024). Brain-inspired computing with fluidic iontronic nanochannels. Proceedings of the National Academy of Sciences, 121(18), e2320242121. https://doi.org/10.1073/pnas.2320242121
Kay, L. E. (2001). From logical neurons to poetic embodiments of mind: Warren S. McCulloch’s project in neuroscience. Science in Context, 14(4), 591–614. https://doi.org/10.1017/s0269889701000266
Kriegeskorte, N. (2015). Deep neural networks: A new framework for modeling biological vision and brain information processing. Annual Review of Vision Science, 1, 417–446. https://doi.org/10.1146/annurev-vision-082114-035447
Loveland, D. W., Hodel, R., & Sterrett, S. G. (2014). Three Views of Logic: Mathematics, philosophy and Computer Science. Princeton University Press. https://doi.org/10.1515/9781400848751
Lytton, W. W. (2007). From computer to brain: Foundations of computational neuroscience. Springer Science & Business Media.
Malabou, C. (2009). What should we do with our brain? Fordham Univ Press.
Marcus, G., Marblestone, A., & Dean, T. (2014). The atoms of neural computation. Science, 346(6209), 551–552. https://doi.org/10.1126/science.1261661
Maturana, H. R., & Varela, F. J. (1991). Autopoiesis and cognition: The realization of the living. Springer Science & Business Media.
McCulloch, W. S., & Pitts, W. (1990). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biology, 52(1), 99–115. https://doi.org/10.1007/BF02459570
Minsky, M. (1988). Society of mind. Simon and Schuster. https://doi.org/10.21236/ADA200313
Piccinini, G. (2004). The First Computational Theory of Mind and Brain: A Close Look at Mcculloch and Pitts’s “Logical Calculus of Ideas Immanent in Nervous Activity.” Synthese, 141(2), 175–215. https://doi.org/10.1023/B:SYNT.0000043018.52445.3e
Putnam, H. (1975). Mind, language and reality. Philosophical Papers (Vol. 2). Cambridge University press. https://doi.org/10.1017/CBO9780511625251
Shannon, C. E. (1938). A symbolic analysis of relay and switching circuits. Electrical Engineering, 57(12), 713–723. https://doi.org/10.1109/EE.1938.6431064
Spitzer, M. (1999). The mind within the net: Models of learning, thinking, and acting. MIT Press. https://doi.org/10.7551/mitpress/4632.001.0001
Thompson, E. (2010). Mind in life: Biology, phenomenology, and the sciences of mind. Harvard University Press.
Vassallo, M., Sattin, D., Parati, E., & Picozzi, M. (2024). Problems of Connectionism. Philosophies, 9(2), 41. https://doi.org/10.3390/philosophies9020041
Végh, J., & Berki, Á. J. (2023). Revisiting neural information, computing and linking capacity. Mathematical Biosciences and Engineering, 20(7), 12380–12403. https://doi.org/10.3934/mbe.2023551
Von Neumann, J. (2012). The computer and the brain. Yale university press.
Walmsley, J. (2016). Mind and machine. Springer.
Wiener, N. (2019). Cybernetics or Control and Communication in the Animal and the Machine. MIT press. https://doi.org/10.7551/mitpress/11810.001.0001
Wittgenstein, L. (2002). Tractatus Logico-Philosophicus. Routledge. https://doi.org/10.4324/9780203010341
Zednik, C. (2021). Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence. Philosophy & Technology, 34(2), 265–288. https://doi.org/10.1007/s13347-019-00382-7
This work is licensed under a Creative Commons Attribution 4.0 International License.