Digital visualization of the activity of neural networks of the human brain before, during, and after insight when images are being recognized

Shelepin, K.Y., Trufanov, G.E., Fokin, V.A., Vasiliev, P.P., Sokolov, A.V.

Full text «Opticheskii Zhurnal»

Full text on elibrary.ru

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Шелепин К.Ю., Труфанов Г.Е., Фокин В.А., Васильев П.П., Соколов А.В. Цифровая визуализация активности нейронных сетей головного мозга человека до, во время и после инсайта при распознавании изображений // Оптический журнал. 2018. Т. 85. № 8. С. 29–38. http://doi.org/10.17586/1023-5086-2018-85-08-29-38

Shelepin K.Yu., Trufanov G.E., Fokin V.A., Vasiliev P.P., Sokolov A.V. Digital visualization of the activity of neural networks of the human brain before, during, and after insight when images are being recognized [in Russian] // Opticheskii Zhurnal. 2018. V. 85. № 8. P. 29–38. http://doi.org/10.17586/1023-5086-2018-85-08-29-38

For citation (Journal of Optical Technology):

K. Yu. Shelepin, G. E. Trufanov, V. A. Fokin, P. P. Vasil’ev, and A. V. Sokolov, "Digital visualization of the activity of neural networks of the human brain before, during, and after insight when images are being recognized," Journal of Optical Technology. 85(8), 468-475 (2018). https://doi.org/10.1364/JOT.85.000468

Abstract:

Technological resources have made it possible to begin experimental study of the activity of neural networks both in the conscious, systematic, analytic method of solving problems, and in sudden, abrupt enlightenment or insight. The purpose of this paper is to study the reconstruction of neural networks when they go from unconscious information processing to the conscious processes that accompany insight. Methods for digitally synthesizing tests (dynamic images) and methods for digitally analyzing images of brain responses can be used to establish the laws that govern the reconstruction of the activity of large-scale neural networks of the human brain at the instant of insight. The activity of large-scale neural networks reaches its maximum at the moment of insight, corresponding to the recognition threshold or the solution of a problem and is less under below-threshold and above-threshold conditions at which dynamic images are observed. The BOLD (blood-oxygen-level-dependent) value is initially maximized when the threshold is reached in some zones and the maximum falloff of the activity of the neural networks is reached in other zones of the brain.

Keywords:

sight, incomplete images, images semantics, recognition threshold, digital image processing, functional magnetic-resonance imaging, large-scale neural networks, cytoarchitectonic Brodmann areas of the brain

Acknowledgements:

The research was supported by the Fundamental Scientific Research of State Academies in 2013–2020 (GP-14, section 63).

OCIS codes: 330.5000, 330.7310, 330.4270, 330.5510

References:

1. R. F. Hess and D. J. Field, “Integration of contours: new insights,” Trends Cognit. Sci. 3(12), 480–486 (1999).
2. D. J. Field, A. Hayes, and R. F. Hess, “Contour integration by the human visual system: evidence for a local association field,” Vis. Res. 33, 173–193 (1993).
3. N. Foreman and R. Hemmings, “The Gollin incomplete-figures test: a flexible, computerised version,” Perception 16(16), 543–548 (1987).
4. K. Yu. Shelepin, S. V. Pronin, and Yu. E. Shelepin, “Recognizing fragmented images and the appearance of ‘insight’,” J. Opt. Technol. 82(10), 700–706 (2015) [Opt. Zh. 82(10), 70–78 (2015)].
5. K. Yu. Shelepin and Yu. E. Shelepin, “Neurophysiology of ‘insight’,” Peterb. Psikhol. Zh. (11), 19–38 (2015).
6. K. Yu. Shelepin, A. V. Sokolov, V. A. Fokin, P. P. Vasil’ev, and S. V. Pronin, “Phenomenon of insight and digital visualization of the activity of the human brain,” Vestn. YuUrGU. Ser. Psikhol. 10(4), 47–55 (2017).
7. V. D. Glezer, Vision and Thought (Nauka, Leningrad, 1995).
8. J. Kounios and M. Beeman, “The cognitive neuroscience of insight,” Annu. Rev. Psychol. 65, 71–93 (2014).
9. A. Ardila, P. H. Bertolucci, L. W. Braga, A. Castro-Caldas, T. Judd, M. H. Kosmidis, E. Matute, R. Nitrini, F. Ostrosky-Solis, and M. Rosselli, “Illiteracy: the neuropsychology of cognition without reading,” Arch. Clin. Neuropsychol. 25, 689–712 (2010).
10. A. Kraft, C. Grimsen, S. Kehrer, M. Bahnemann, K. Spang, M. Prass, K. Irlbacher, M. Köhnlein, A. Lipfert, F. Brunner, A. Kastrup, M. Fahle, and S. A. Brandt, “Neurological and neuropsychological characteristics of occipital, occipito-temporal and occipito-parietal infarction,” Cortex 56, 38–50 (2006).
11. Yu. E. Shelepin, V. A. Fokin, S. V. Men’shikova, O. V. Borachuk, S. A. Koskin, A. V. Sokolov, S. V. Pronin, A. K. Kharauzov, P. P. Vasil’ev, and O. A. Vakhrameeva, “Methods of iconics and methods of mapping the brain when estimating the functional state of the visual system,” Sens. Sist. 28(2), 63–78 (2014).
12. T. L. Saaty, “The analytic hierarchy and analytic network measurement processes: applications to decisions under risk,” Eur. J. Pure Appl. Math. 1(1), 122–196 (2008).
13. H. Wechsler, Neural Networks for Perception: Human and Machine Perception (Academic Press, New York, 2014).
14. J. Weng, S. Paslaski, J. Daly, C. Van Dam, and J. Brown, “Modulation for emergent networks: serotonin and dopamine,” Neural Networks 41, 225–239 (2013).