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Warianty tytułu
Języki publikacji
Abstrakty
Structural plasticity in the brain (i.e. rewiring the connectome) may be viewed as mechanisms for dynamic reconfiguration of neural circuits. First order computations in the brain are done by static neural circuits, whereas higher order computations are done by dynamic reconfigurations of the links (synapses) between the neural circuits. Static neural circuits correspond to first order computable functions. Synapse creation (activation) between them correspond to the mathematical notion of function composition. Functionals are higher order functions that take functions as their arguments. The construction of functionals is based on dynamic reconfigurations of function compositions. Perhaps the functionals correspond to rewiring mechanisms of the connectome. The architecture of human mind is different than the von Neumann computer architecture. Higher order computations in the human brain (based on functionals) may suggest a non-von Neumann computer architecture, a challenge posed by John Backus in 1977 [7]. The presented work is a substantial extension and revision of [2].
Rocznik
Tom
Strony
5--20
Opis fizyczny
Bibliogr. 34 poz., rys.
Twórcy
autor
- University of Siedlce, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland
Bibliografia
- 1. Adolphs, R.: The unsolved problems of neuroscience. Trends in cognitive sciences 19(4), 173–175(2015).
- 2. Ambroszkiewicz, S.: On higher order computations and synaptic meta-plasticity in the human brain.In: International Conference on Artificial Neural Networks. pp. 145–152. Springer (2016).
- 3. Ambroszkiewicz, S.: Functionals and hardware. arxiv.org/abs/1501.03043 (2015), arxiv.org/abs/1501.03043
- 4. Ambroszkiewicz, S.: The grounding for continuum. arxiv.org/abs/1510.02787 (2015), https://arxiv.org/abs/1510.02787
- 5. Ambroszkiewicz, S.: On the notion of “von neumann vicious circle” coined by john backus. http://arxiv.org/abs/1602.02715 (2016), http://arxiv.org/abs/1602.02715
- 6. Ambroszkiewicz, S.: Combinatorial constructions of intrinsic geometries. arxiv.org/abs/1904.05173 (2020), https://arxiv.org/abs/1904.05173
- 7. Backus, J.: Can programming be liberated from the von neumann style?: A functional style and its algebra of programs. Commun. ACM 21(8), 613–641 (Aug 1978). https://doi.org/10.1145/359576.359579, http://doi.acm.org/10.1145/359576.359579
- 8. Bertolero, M.A., Yeo, B.T., D. Esposito, M.: The modular and integrative functional architecture of the human brain. Proceedings of the National Academy of Sciences 112(49), E6798–E6807 (2015).
- 9. Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J.I., Grimm, O., Heinz, A., Tost, H., et al.: Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proceedings of the National Academy of Sciences 112(37), 11678–11683 (2015).
- 10. Bron, D.: Brain Computers? . https://medium.com/chain-reaction/brain-computers-976ae7ec19a8 (Oct 2023), https://medium.com/chain-reaction/brain-computers-976ae7ec19a8medium.com
- 11. Capalija, D., Abdelrahman, T.S.: Tile-based bottom-up compilation of custom mesh-of-functionalunits fpga overlays. In: 2014 24th International Conference on Field Programmable Logic and Applications (FPL). pp. 1–8. IEEE (2014).
- 12. Cong, J., Huang, H., Ma, C., Xiao, B., Zhou, P.: A fully pipelined and dynamically composable architecture of cgra. In: Field-Programmable Custom Computing Machines (FCCM), 2014 IEEE 22nd Annual International Symposium on. pp. 9–16. IEEE (2014).
- 13. De Sutter, B., Raghavan, P., Lambrechts, A.: Coarse-grained reconfigurable array architectures. In: Handbook of signal processing systems, pp. 553–592. Springer (2013)
- 14. Harris, J.A., Mihalas, S., Hirokawa, K.E., Whitesell, J.D., Choi, H., Bernard, A., Bohn, P., Caldejon, S., Casal, L., Cho, A., et al.: Hierarchical organization of cortical and thalamic connectivity. Nature 575(7781), 195–202 (2019).
- 15. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences 79(8), 2554–2558 (1982).
- 16. Hopfield, J.J., Tank, D.W., et al.: Computing with neural circuits- a model. Science 233(4764), 625–633 (1986).
- 17. Jain, A.K., Li, X., Fahmy, S.A., Maskell, D.L.: Adapting the dyser architecture with dsp blocks as an overlay for the xilinx zynq. ACM SIGARCH Computer Architecture News 43(4), 28–33 (2016).
- 18. Johnson, R.C.: Non-von neumann computers providing brain-like functionality. Communications of theACM(Nov 2016), https://cacm.acm.org/news/non-von-neumann-computersproviding-brain-like-functionality/
- 19. Kanold, P.O., Deng, R., Meng, X.: The integrative function of silent synapses on subplate neurons in cortical development and dysfunction. Frontiers in Neuroanatomy 13, 41 (2019). https://doi.org/10.3389/fnana.2019.00041, https://www.frontiersin.org/article/10.3389/fnana.2019.00041
- 20. Kiverstein, J., Miller, M.: The embodied brain: Towards a radical embodied cognitive neuroscience. Frontiers in Human Neuroscience 9(237) (2015). https://doi.org/10.3389/fnhum.2015.00237, http://www.frontiersin.org/human_neuroscience/10.3389/fnhum.2015.00237/abstract
- 21. Leopold, D.A., Strick, P.L., Bassett, D.S., Bruno, R.M., Cuntz, H., Harris, K.M., Oberlaender, M., Raichle, M.E.: Functional architecture of the cerebral cortex. In: The Neocortex, vol. 27, pp. 141–164. MIT Press (2019).
- 22. Lyke, J.C., Christodoulou, C.G., Vera, G.A., Edwards, A.H.: An introduction to reconfigurable systems. Proceedings of the IEEE 103(3), 291–317 (2015).
- 23. Ma, S., Aklah, Z., Andrews, D.: Just in time assembly of accelerators. In: Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. pp. 173–178. ACM (2016).
- 24. Matyja, J.R., Dolega, K.: Radical embodied neuroscience - how and why? a commentary on: The embodied brain: Towards a radical embodied cognitive neuroscience, front. hum. neurosci. 06 may 2015, http://dx.doi.org/10.3389/fnhum.2015.00237. Frontiers in Human Neuroscience 9(669) (2015). https://doi.org/10.3389/fnhum.2015.00669, http://www.frontiersin.org/human_neuroscience/10.3389/fnhum.2015.00669/full
- 25. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 5(4), 115–133 (1943)
- 26. Mukherjee, M., Fell, A., Guha, A.: Dfgentool: A dataflow graph generation tool for coarse grain reconfigurable architectures. In: 2017 30th International Conference on VLSI Design and 2017 16th International Conference on Embedded Systems (VLSID). pp. 67–72 (Jan 2017). https: //doi.org/10.1109/VLSID.2017.62
- 27. von Neumann, J.: The Computer and the Brain. Yale University Press, New Haven, CT, USA (1958).
- 28. von Neumann, J., Burks, A.W., et al.: Theory of self-reproducing automata. IEEE Transactions on Neural Networks 5(1), 3–14 (1966).
- 29. Niedermeier, A., Kuper, J., Smit, G.J.: A dataflow inspired programming paradigm for coarsegrained reconfigurable arrays. In: International Symposium on Applied Reconfigurable Computing. pp. 275–282. Springer (2014).
- 30. Palumbo, F., Sau, C., Fanni, T., Meloni, P., Raffo, L.: Dataflow-based design of coarse-grained reconfigurable platforms. In: Signal Processing Systems (SiPS), 2016 IEEE InternationalWorkshop on. pp. 127–129. IEEE (2016).
- 31. Park, H.J., Friston, K.: Structural and functional brain networks: from connections to cognition. Science 342(6158), 1238411 (2013).
- 32. Sanz-Leon, P., Knock, S.A., Spiegler, A., Jirsa, V.K.: Mathematical framework for largescale brain network modeling in the virtual brain. NeuroImage 111, 385 – 430 (2015). https://doi.org/http://dx.doi.org/10.1016/j.neuroimage.2015.01.002, http://www.sciencedirect.com/science/article/pii/S1053811915000051
- 33. Siegelmann, H.T., Sontag, E.D.: On the computational power of neural nets. Journal of computer and system sciences 50(1), 132–150 (1995).
- 34. Tessier, R., Pocek, K., DeHon, A.: Reconfigurable computing architectures. Proceedings of the IEEE 103(3), 332–354 (2015).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-72a6d762-41e7-4dcb-a4d1-60d88f42084b
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