No Access

Number of Autonomous Cognitive Agents in a Neural Network

Subhash Kak

Oklahoma State University, Stillwater, OK 74078, USA

https://doi.org/10.1142/S2705078522500023Cited by:7 (Source: Crossref)

Abstract

This paper considers several aspects of the relationship between size, structure, speed of propagation and the number of autonomous cognitive agents in a neural network. While memory and function generation capacities of neural networks with scale-invariant structure have been investigated extensively, the number of autonomous agents, associated with the simultaneous processing of the independent components of the data, has not received prior attention. We propose the emergence of the dichotomy of causal and noncausal regions created by the speed of propagation, in which the autonomous cognitive agents are not bound in a causal relationship with other agents. Arguments are presented for why the count of autonomous agents is best estimated with respect to the dimensionality of the underlying space. The number of autonomous agents obtained for the human brain equals 25, and it is significant that the number in the sub-system modules also turns out to be close to the same value. This number equals the number of enumerative categories of reality in at least one philosophical tradition, and this coincidence may be attributed to the capacity of consciousness to reflect on its own structure. It is possible that near equality across layers provides a special uniqueness to the human brain. We propose that the findings of this study will be useful in the design of neural-network-based AI systems that are designed to emulate human cognitive capacity.

Keywords:

References

Azevedo, F. A., Carvalho, L. R., Grinberg, L. T. et al.. [2009] Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain, J. Comput. Neurol. 513(5), 532–541. Crossref, Google Scholar
Baldi, P. and Vershynin, R. [2019] The capacity of feedforward neural networks, arXiv:1901.00434. Google Scholar
Bullmore, E. and Sporns, O. [2012] The economy of brain network organization, Nat. Rev. Neurosci. 13, 336–349. Crossref, Google Scholar
Buxhoeveden, D. P. and Casanova, M. F. [2002] The minicolumn hypothesis in neuroscience, Brain 125, 935–951. Crossref, Google Scholar
Chella, A., Frixione, M. and Gaglio, S. [2008] A cognitive architecture for robot self-consciousness, Artif. Intell. Med. 44, 147–154. Crossref, Google Scholar
Cuntz, H., Mathy, A. and Häusser, M. [2012] A scaling law derived from optimal dendritic wiring, Proc. Natl. Acad. Sci. 109, 11014–11018. Crossref, Google Scholar
Cytowic, R. E. [2002] Synesthesia: A Union of the Senses, 2nd ed. (MIT Press, Cambridge, MA). Crossref, Google Scholar
Deco, G., Vidaurre, D. and Kringelbach, M. L. [2021] Revisiting the global workspace orchestrating the hierarchical organization of the human brain, Nat. Human Behav. https://doi.org/10.1038/s41562-020-01003-6. Crossref, Google Scholar
Dehaene, S., Kerszberg, M. and Changeux, J.-P. [1998] A neuronal model of a global workspace in effortful cognitive tasks, Proc. Natl. Acad. Sci. 95(24), 14529–14534. Crossref, Google Scholar
Dowling, J. E. [2001] Neurons, and Networks: An Introduction to Behavioral Neuroscience (Harvard University Press). Google Scholar
Falconer, K. J. [2003] Fractal Geometry: Mathematical Foundations and Applications (Wiley). Crossref, Google Scholar
Felleman, D. J. and Van Essen, D. C. [1991] Distributed hierarchical processing in the primate cerebral cortex, Cereb. Cortex 1, 1–47. Crossref, Google Scholar
Gamez, D. [2018] Human and Machine Consciousness (Open Book Publishers). Crossref, Google Scholar
Gibson, J. J. [1979] The Ecological Approach to Visual Perception (Houghton Mifflin, Boston). Google Scholar
Guliyev, N. and Ismailov, V. [2018] Approximation capability of two hidden layer feedforward neural networks with fixed weights, Neurocomputing 316, 262–269. Crossref, Google Scholar
Herculano-Houzel, S. [2009] The human brain in numbers: A linearly scaled-up primate brain, Front. Human Neurosci. 3, 31. Crossref, Google Scholar
Kaas, J. H. [2000] Why is brain size so important, Brain Mind 1, 7–23. Crossref, Google Scholar
Kak, A., Gautam, A. and Kak, S. [2016] A three-layered model for consciousness states, NeuroQuantol. 14, 166–174. Crossref, Google Scholar
Kak, S. [2000] Active agents, intelligence and quantum computing, Inf. Sci. 128, 1–17. Crossref, Google Scholar
Kak, S. [2016] The Loom of Time (DK Printworld, New Delhi). Google Scholar
Kak, S. [2020] Information theory and dimensionality of space, Sci. Rep. 10, 20733. Crossref, Google Scholar
Kak, S. [2021a] Asymptotic freedom and noninteger dimensionality, Sci. Rep. 11, 1–5. https://www.nature.com/articles/s41598-021-83002-9 Crossref, Google Scholar
Kak, S. [2021b] The intrinsic dimensionality of data, Circuits Syst. Signal Process. 40, 2599–2607. Crossref, Google Scholar
Kak, S. [2021c] Fractals with optimal information dimension, Circuits Syst. Signal Process. 40, 5733–5743. Crossref, Google Scholar
Kak, S. [2021d] The base-e representation of numbers and the power law, Circuits Syst. Signal Process. 40, 490–500. Crossref, Google Scholar
Kak, S. [2021e] The e-dimensionality of genetic information, TechRxiv, https://doi.org/10.36227/techrxiv.14977479.v1. Google Scholar
Kak, S. [2022a] The limits to machine consciousness, J. Artif. Intell. Consciousness 9, 59–72. Link, Google Scholar
Kak, S. [2022b] The iterated Newcomb-Benford distribution for structured systems. Int. J. Appl. Comput. Math. 8, 51. https://doi.org/10.1007/s40819-022-01251-2 Crossref, Google Scholar
Kak, S. [2022c] New classes of regular symmetric fractals, Circuits Syst. Signal Process. 41. https://link.springer.com/article/10.1007/s00034-022-01966-z Crossref, Google Scholar
Kislyuk, D. S., Möttönen, R. and Sams, M. [2008] Visual processing affects the neural basis of auditory discrimination, J. Cogn. Neurosci. 20, 2175–2184. https://doi.org/10.1162/jocn.2008.20152 Crossref, Google Scholar
Kriegeskorte, N. and Douglas, P. K. [2018] Cognitive computational neuroscience, Nat. Neurosci. 21, 1148–1160. Crossref, Google Scholar
Krohn, S. et al.. [2019] Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI, Human Brain Mapp. 40, 3299–3320. Crossref, Google Scholar
Larson, G. J., Bhattacharya, R. S. and Potter, K. [2014] The Encyclopedia of Indian Philosophies, Vol. 4 (Princeton University Press, Princeton). Google Scholar
Madl, T., Baars, B. J. and Franklin, S. [2011] The timing of the cognitive cycle. PloS One 6(4), e14803. Crossref, Google Scholar
Markov, N. T. et al.. [2014] Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex, J. Comput. Neurol. 522, 225–259. Crossref, Google Scholar
Martins M. D. [2012] Distinctive signatures of recursion, Philos. Trans. Royal Soc. London. B. Biol. Sci. 367(1598), 2055–2064. Crossref, Google Scholar
Mountcastle, V. B. [1997] The columnar organization of the neocortex, Brain 120, 701–722. Crossref, Google Scholar
Pinson, A., Namba, T. and Huttner, W. B. [2019] Malformations of human neocortex in development — Their progenitor cell basis and experimental model systems, Front. Cell. Neurosci. 13, 305. Crossref, Google Scholar
Platek, S. M. and Shackelford, T. K. [2009] Foundations in Evolutionary Cognitive Neuroscience (Cambridge University Press, UK). Crossref, Google Scholar
Prados, D. and Kak, S. [1989a] Neural network capacity using the delta rule, Electron. Lett. 25, 197–199. Crossref, Google Scholar
Prados, D. and Kak, S. [1989b] Non-binary neural networks, Lect. Notes Comput. Control 130, 97–104. Crossref, Google Scholar
Rakic, P. [2009] Evolution of the neocortex: A perspective from developmental biology, Nat. Rev. Neurosci. 10, 724–735. Crossref, Google Scholar
Reason, C. M. [2016] Consciousness is not a physically provable property, J. Mind Behav. 37(1), 31–46. Google Scholar
Reishofer, G., Studencnik, F., Koschutnig, K. et al.. [2018] Age is reflected in the fractal dimensionality of MRI diffusion based tractography, Sci. Rep. 8, 5431. Crossref, Google Scholar
Schrödinger, E. [1944] What is Life? The Physical Aspect of the Living Cell (Cambridge University Press). Google Scholar
Shimono, M. and Hatano, N. [2018] Efficient communication dynamics on macro-connectome, and the propagation speed, Sci. Rep. 8, 2510. Crossref, Google Scholar
Siegel, A. and Sapru, H. N. [2006] Essential Neuroscience (Lippincott Williams & Wilkins). Google Scholar
Simner, J. [2012] Defining synaesthesia, Br. J. Psychol. 103, 1–15. Crossref, Google Scholar
Tomasi, D. G., Shokri-Kojori, E. and Volkow, N. D. [2017] Brain network dynamics adhere to a power law, Front. Neurosci. 11, 72. Crossref, Google Scholar
Van den Heuvel, M. P. and Sporns, O. [2011] Rich-club organization of the human connectome, J. Neurosci. 31, 15775–15786. Crossref, Google Scholar
Yara, K. et al.. [2017] Scale invariance in natural and artificial collective systems: A review, J. R. Soc. Interface 14, 20170662. Crossref, Google Scholar