Advertisement

A neural network model for exogenous perceptual alternations of the Necker cube

  • Osamu ArakiEmail author
  • Yuki Tsuruoka
  • Tomokazu Urakawa
Research Article
  • 33 Downloads

Abstract

When a bistable visual image, such as the Necker cube, is continuously viewed, the percept of the image endogenously alternates between one possible percept and the other. However, perceptual alternation can also be induced by an exogenous perturbation. For example, a typical external perturbation is the flashlight, which is expected to pervasively activate many brain regions. Therefore, the neural mechanism related to exogenous perceptual alternation remains to be clarified. As a cue to solving this problem, our recent psychophysiological experiment reported a positive correlation between the enhancement of visual mismatch negativity evoked by breaks in the sequential regularity of the visual stimuli and the proportion of perceptual alternation. To elucidate the mechanism underlying exogenous perceptual alternation induced by visual mismatch negativity, the present study attempted to construct a neural network model for bistable perception of the Necker cube, whose perceptual alternation is facilitated by an increase in visual mismatch negativity. The model consists of both a prediction layer and a prediction error layer, following the predictive coding framework for biologically plausible relationships between the change detection process and the perceptual alternation mechanism. Computer simulations showed that the mean duration of perception decreased as the response increased, which is in concordance with the experimental data. This result suggested that the excitatory feedforward and inhibitory feedback connections play an important role. Additionally, the validity of this model suggests that the visual mismatch signal propagates in the neural systems and affects the visual perceptual mechanism as a prediction error signal.

Keywords

Necker cube Perceptual alternation Neural network model Predictive coding 

Notes

References

  1. Buesing L, Bill J, Nessler B, Maass W (2011) Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. PLoS Comput Biol 7(11):e1002211PubMedPubMedCentralCrossRefGoogle Scholar
  2. Brascamp JW, Klink PC, Levelt WJM (2015) The laws of binocular rivalry: 50 years of Levelt’s propositions. Vis Res 109:20–37PubMedCrossRefGoogle Scholar
  3. Chikkerur S, Serre T, Tan C, Poggio T (2010) What and where: a Bayesian inference theory of attention. Vis Res 50(22):2233–2247PubMedCrossRefGoogle Scholar
  4. Clark A (2013) Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav Brain Sci 36(3):181–204PubMedCrossRefGoogle Scholar
  5. Curtu R, Shpiro A, Rubin N, Rinzel J (2008) Mechanisms for frequency control in neuronal competition models. SIAM J Appl Dyn Syst 7(2):609–649PubMedPubMedCentralCrossRefGoogle Scholar
  6. Dayan P (1998) A hierarchical model of binocular rivalry. Neural Comput 10(5):1119–1135PubMedCrossRefGoogle Scholar
  7. Friston K (2003) Learning and inference in the brain. Neural Netw 16(9):1325–1352PubMedCrossRefGoogle Scholar
  8. Friston K (2005) A theory of cortical responses. Philos T Roy Soc B 360(1456):815–836CrossRefGoogle Scholar
  9. Gershman SJ, Vul E, Tenenbaum JB (2012) Multistability and perceptual inference. Neural Comput 24(1):1–24PubMedCrossRefGoogle Scholar
  10. Grossber S, Swaminathan G (2004) A laminar cortical model for 3D perception of slanted and curved surfaces and of 2D images: development, attention, and bistability. Vis Res 44(11):1147–1187CrossRefGoogle Scholar
  11. Hohwy J, Roepstorff A, Friston K (2008) Predictive coding explains binocular rivalry: an epistemological review. Cognition 108(3):687–701PubMedCrossRefGoogle Scholar
  12. Kanai R, Moradi F, Shimojo S, Verstraten FAJ (2005) Perceptual alternation induced by visual transients. Perception 34(7):803–822PubMedCrossRefGoogle Scholar
  13. Laing CR, Chow CC (2002) A spiking neuron model for binocular rivalry. J Comput Neurosci 12(1):39–53PubMedCrossRefGoogle Scholar
  14. Lehky S (1988) An astable multivibrator model of binocular rivalry. Perception 17(2):215–228PubMedCrossRefGoogle Scholar
  15. Leopold DA, Logothetis NK (1999) Multistable phenomena: changing views in perception. Trends Cogn Sci 3(7):254–264PubMedCrossRefGoogle Scholar
  16. Levelt WJM (1968) On binocular rivalry. Mouton, ParisGoogle Scholar
  17. Lieder F, Daunizeau J, Garrido MI, Friston KJ, Stephan KE (2013a) Modelling trial-by-trial changes in the mismatch negativity. PLoS Comput Biol 9(2):e1002911PubMedPubMedCentralCrossRefGoogle Scholar
  18. Lieder F, Stephan KE, Daunizeau J, Garrido MI, Friston KJ (2013b) A neurocomputational model of the mismatch negativity. PLoS Comput Biol 9(11):e1003288PubMedPubMedCentralCrossRefGoogle Scholar
  19. Matsuoka K (1984) The dynamic model of binocular rivalry. Biol Cybern 49(3):201–208PubMedCrossRefGoogle Scholar
  20. Moreno-Bote R, Rinzel J, Rubin N (2007) Noise-induced alternations in an attractor network model of perceptual bistability. J Neurophysiol 98(3):1125–1139PubMedPubMedCentralCrossRefGoogle Scholar
  21. Moreno-Bote R, Shpiro A, Rinzel J, Rubin N (2010) Alternation rate in perceptual bistability is maximal at and symmetric around eqi-dominance. J Vis 10(11):1–18PubMedPubMedCentralCrossRefGoogle Scholar
  22. Panagiotaropoulos TI, Kapoor V, Logothetis NK, Deco G (2013) A common neurodynamical mechanism could mediate externally induced and intrinsically generated transitions in visual awareness. PLoS ONE 8(1):e53833PubMedPubMedCentralCrossRefGoogle Scholar
  23. Pisarchik AN, Jaimes-Reátegui R, Magallón-García CDA, Castillo-Morales CO (2014) Critical slowing down and noise-induced intermittency in bistable perception: bifurcation analysis. Biol Cybern 108(4):397–404PubMedCrossRefGoogle Scholar
  24. Platonov A, Goossens J (2013) Influence of contrast and coherence on the temporal dynamics of binocular motion rivalry. PLoS ONE 8(8):e71931PubMedPubMedCentralCrossRefGoogle Scholar
  25. Risken H (1996) Fokker–Planck equation, 2nd edn. Springer, BerlinGoogle Scholar
  26. Runnova AE, Hramov AE, Grubov VV, Koronovskii AA, Kurovskaya MK, Pisarchik AN (2016) Theoretical background and experimental measurements of human brain noise intensity in perception of ambiguous images. Chaos Soliton Fract 93:201–206CrossRefGoogle Scholar
  27. Shpiro A, Curtu R, Rinzel J, Rubin N (2007) Dynamical characteristics common to neuronal competition models. J Neurophysiol 97(1):462–473PubMedCrossRefGoogle Scholar
  28. Shpiro A, Moreno-Bote R, Rubin N, Rinzel J (2009) Balance between noise and adaptation in competition models of perceptual bistability. J Comput Neurosci 27(1):37–54PubMedPubMedCentralCrossRefGoogle Scholar
  29. Spratling MW (2016) Predictive coding as a model of cognition. Cogn Process 17(3):279–305PubMedCrossRefGoogle Scholar
  30. Stefanics G, Kremláček J, Czigler I (2014) Visual mismatch negativity: a predictive coding view. Front Hum Neurosci 8:1–19CrossRefGoogle Scholar
  31. Sterzer P, Kleinschmidt A, Rees G (2009) The neural bases of multistable perception. Trends Cogn Sci 13(7):310–318PubMedCrossRefGoogle Scholar
  32. Stollenwerk L, Bode M (2003) Lateral neural model of binocular rivalry. Neural Comput 15(12):2863–2882PubMedCrossRefGoogle Scholar
  33. Sundareswars R, Schrater PR (2008) Perceptual multistability predicted by search model for Bayesian decisions. J Vis 8(5):1–19CrossRefGoogle Scholar
  34. Urakawa T, Bunya M, Araki O (2017a) Involvement of the visual change detection process in facilitating perceptual alternation in the bistable image. Cogn Neurodyn 11(4):307–318PubMedPubMedCentralCrossRefGoogle Scholar
  35. Urakawa T, Aragaki T, Araki O (2017b) Exogenously-driven perceptual alternation of a bistable image: from the perspective of the visual change detection process. Neurosci Lett 653:92–96PubMedCrossRefGoogle Scholar
  36. Wacongne C, Changeux J-P, Dehaene S (2012) A neuronal model of predictive coding accounting for the mismatch negativity. J Neurosci 32(11):3665–3678PubMedPubMedCentralCrossRefGoogle Scholar
  37. Wilson HR (2007) Minimal physiological conditions for binocular rivalry and rivalry memory. Vis Res 47(21):2741–2750PubMedCrossRefGoogle Scholar
  38. Zhou YH, Gao JB, White KD, Merk I, Yao K (2004) Perceptual dominance time distributions in multistable visual perception. Biol Cybern 90(4):256–263PubMedCrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Applied PhysicsTokyo University of ScienceTokyoJapan

Personalised recommendations