ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Tsukerman V. D. Neurodynamic model for creative cognition of relational networks with even cyclic inhibition. Izvestiya VUZ. Applied Nonlinear Dynamics, 2022, vol. 30, iss. 3, pp. 331-357. DOI: 10.18500/0869-6632-2022-30-3-331-357

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text:
(downloads: 269)
Article type: 

Neurodynamic model for creative cognition of relational networks with even cyclic inhibition

Tsukerman Valery Davidovich, Southern Federal University

The purpose of this work is study of the neurodynamic foundations of the creative activity of the brain. Modern AI systems using deep neural network training require large amounts of input data, high computational costs and long training times. On the contrary, the brain can learn from small datasets in no time and, crucially, it is fundamentally creative. Methods. The study was carried out through computational experiments with neural networks containing 5 and 7 oscillatory layers (circuits) trained to represent abstract concepts of a certain class of animals. The scheme of neural networks with even cyclic inhibition (ECI networks) contains only bilateral inhibitory connections and consists of two subnets: a reference noncoding network, which is an analogue of the default brain mode neural network, and the main information network that receives time sequences of environmental signals and contextual inputs. After training, the reading of the population phase codes was performed with a simple linear decoder. Results. Conceptual learning of the network leads to the generation of a number of spatial abstract images that are distinguished by the most pronounced features of the relevant line of animals. In computational experiments, a wide set of isomorphic representations of concepts was obtained through: a) transformations of image spaces in a wide range of time scales of the training input signal flow, b) internal regulation of the time scales of mental representations of concepts, c) confirmation on the model of the dependence of psychological proximity of concepts on semantic distance; d) calling from memory (decoding) distributed groups of neurons of animal concepts, which the network has not been trained in. Conclusion. This paper shows for the first time how, using a small set of event input data (a sequence of 4 CCW and 2 CW signals) and very limited computational resources, ECI networks exhibit creative cognitions based on relational relationships, conceptual learning and generalization of knowledge.

  1. Abraham A. The promises and perils of the neuroscience of creativity. Front. Hum. Neurosci. 2013;7:246. DOI: 10.3389/fnhum.2013.00246.
  2. Benedek M, Fink A. Toward a neurocognitive framework of creative cognition: the role of memory, attention, and cognitive control. Curr. Opin. Behav. Sci. 2019;27:116–122. DOI: 10.1016/j.cobeha.2018.11.002.
  3. Kenett YN, Faust M. A semantic network cartography of the creative mind. Trends Cogn. Sci. 2019;23(4):271–274. DOI: 10.1016/j.tics.2019.01.007.
  4. Beaty RE, Chen Q, Christensen AP, Kenett YN, Silvia PJ, Benedek M, Schacter DL. Default network contributions to episodic and semantic processing during divergent creative thinking: A representational similarity analysis. NeuroImage. 2020;209:116499. DOI: 10.1016/j.neuroimage.2019.116499.
  5. Vigano S, Piazza M. Distance and direction codes underlie navigation of a novel semantic space in the human brain. J. Neurosci. 2020;40(13):2727–2736. DOI: 10.1523/JNEUROSCI.1849-19.2020.
  6. Theves S, Fernandez G, Doeller CF. The hippocampus maps concept space, not feature space. J. Neurosci. 2020;40(38):7318–7325. DOI: 10.1523/JNEUROSCI.0494-20.2020.
  7. Behrens TEJ, Muller TH, Whittington JCR, Mark S, Baram AB, Stachenfeld KL, Kurth-Nelson Z. What is a cognitive map? Organizing knowledge for flexible behavior. Neuron. 2018;100(2):490– 509. DOI: 10.1016/j.neuron.2018.10.002.
  8. Bottini R, Doeller CF. Knowledge across reference frames: Cognitive maps and image spaces. Trends Cogn. Sci. 2020;24(8):606–619. DOI: 10.1016/j.tics.2020.05.008.
  9. Kay K, Chung JE, Sosa M, Schor JS, Karlsson MP, Larkin MC, Liu DF, Frank LM. Constant subsecond cycling between representations of possible futures in the hippocampus. Cell. 2020;180(3): 552–567. DOI: 10.1016/j.cell.2020.01.014.
  10. Raffaelli Q, Wilcox R, Andrews-Hanna J. The neuroscience of imaginative thought: An integrative framework. In: Abraham A, editor. The Cambridge Handbook of the Imagination. Cambridge: Cambridge University Press; 2020. P. 332–353. DOI: 10.1017/9781108580298.021.
  11. Amalric M, Wang L, Pica P, Figueira S, Sigman M, Dehaene S. The language of geometry: Fast comprehension of geometrical primitives and rules in human adults and preschoolers. PLoS Comput. Biol. 2017;13(1):e1005273. DOI: 10.1371/journal.pcbi.1005273.
  12. Tsukerman VD, Cheshkov GN. Fundamentals of nonlinear dynamics of sensory perception. I. Phase coding in oscillatory networks. Neurocomputers: Development, Application. 2002; (7–8):65–72 (in Russian).
  13. Tsukerman VD. Mathematical model of phase coding of events in the brain. Mathematical Biology and Bioinformatics. 2006;1(1):97–107 (in Russian). DOI: 10.17537/2006.1.97.
  14. Tsukerman VD, Eremenko ZS, Karimova OV, Kulakov SV, Sazykin AA. Cognitive neurodynamics two strategies navigation behavior of organisms. Izvestiya VUZ. Applied Nonlinear Dynamics. 2011;19(6):96–108 (in Russian). DOI: 10.18500/0869-6632-2011-19-6-96-108.
  15. O’Keefe J, Dostrovsky J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 1971;34(1):171–175. DOI: 10.1016/0006- 8993(71)90358-1.
  16. Hafting T, Fyhn M, Molden S, Moser MB, Moser EI. Microstructure of a spatial map in the entorhinal cortex. Nature. 2005;436(7052):801–806. DOI: 10.1038/nature03721.
  17. Bellmund JLS, Gardenfors P, Moser EI, Doeller CF. Navigating cognition: Spatial codes for human thinking. Science. 2018;362(6415):eaat6766. DOI: 10.1126/science.aat6766.
  18. Bernardi S, Benna MK, Rigotti M, Munuera J, Fusi S, Salzman CD. The geometry of abstraction in hippocampus and prefrontal cortex. Cell. 2020;183(4):954–967. DOI: 10.1016/j.cell.2020.09.031.
  19. Kim H. Default network activation during episodic and semantic memory retrieval: A selective meta-analytic comparison. Neuropsychologia. 2016;80:35–46. DOI: 10.1016/j.neuropsychologia.2015.11.006.
  20. Marron TR, Lerner Y, Berant E, Kinreich S, Shapira-Lichter I, Hendler T, Faust M. Chain free association, creativity, and the default mode network. Neuropsychologia. 2018;118:40–58. DOI: 10.1016/j.neuropsychologia.2018.03.018.
  21. Doeller CF, Barry C, Burgess N. Evidence for grid cells in a human memory network. Nature. 2010;463(7281):657–661. DOI: 10.1038/nature08704.
  22. Sharp PE, Blair HT, Cho J. The anatomical and computational basis of the rat head-direction cell signal. Trends Neurosci. 2001;24(5):289–294. DOI: 10.1016/S0166-2236(00)01797-5.
  23. Sargolini F, Fyhn M, Hafting T, McNaughton BL, Witter MP, Moser MB, Moser EI. Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science. 2006;312(5774):758– 762. DOI: 10.1126/science.1125572.
  24. Taube JS. The head direction signal: Origins and sensory-motor integration. Annu. Rev. Neurosci. 2007;30:181–207. DOI: 10.1146/annurev.neuro.29.051605.112854.
  25. Rolls ET, Stringer SM. Spatial view cells in the hippocampus, and their idiothetic update based on place and head direction. Neural Networks. 2005;18(9):1229–1241. DOI: 10.1016/j.neunet.2005.08.006.
  26. Rolls ET, Xiang JZ. Spatial view cells in the primate hippocampus and memory recall. Rev. Neurosci. 2006;17(1–2):175–200. DOI: 10.1515/REVNEURO.2006.17.1-2.175.
  27. Tsukerman VD, Kharybina ZS, Kulakov SV. A mathematical model of hippocampal spatial encoding. II. Neurodynamic correlates of mental trajectories and decision-making problem. Mathematical Biology and Bioinformatics. 2014;9(1):216–256 (in Russian). DOI: 10.17537/2014.9.216.
  28. Tsukerman VD. Towards creative cognition: the creative beginnings of relational neural networks with even cyclic inhibition. In: Proceedings of the VII All-Russian Conference «Nonlinear Dynamics in Cognitive Research-2021». Nizhny Novgorod, 20-24 September 2021. Nizhny Novgorod: IAP RAS; 2021. P. 186–189 (in Russian).
  29. Wang J, Narain D, Hosseini EA, Jazayeri M. Flexible timing by temporal scaling of cortical responses. Nat. Neurosci. 2018;21(1):102–110. DOI: 10.1038/s41593-017-0028-6.
  30. Egger SW, Remington ED, Chang CJ, Jazayeri M. Internal models of sensorimotor integration regulate cortical dynamics. Nat. Neurosci. 2019;22(11):1871–1882. DOI: 10.1038/s41593-019- 0500-6.
  31. Raichle ME. The brain’s default mode network. Annu. Rev. Neurosci. 2015;38:433–447. DOI: 10.1146/annurev-neuro-071013-014030. 
  32. Higgins C, Liu Y, Vidaurre D, Kurth-Nelson Z, Dolan R, Behrens T, Woolrich M. Replay bursts in humans coincide with activation of the default mode and parietal alpha networks. Neuron. 2021;109(5):882–893. DOI: 10.1016/j.neuron.2020.12.007.
  33. Peer M, Brunec IK, Newcombe NS, Epstein RA. Structuring knowledge with cognitive maps and cognitive graphs. Trends Cogn. Sci. 2021;25(1):37–54. DOI: 10.1016/j.tics.2020.10.004.
  34. Schacter DL, Addis DR, Buckner RL. Remembering the past to imagine the future: the prospective brain. Nat. Rev. Neurosci. 2007;8(9):657–661. DOI: 10.1038/nrn2213.