Analysis of Neural Circuit for Visual Attention Using Lognormally Distributed Input

  • Yoshihiro Nagano
  • Norifumi Watanabe
  • Atsushi Aoyama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

Visual attention has recently been reported to modulate neural activity of narrow spiking and broad spiking neurons in V4, with increased firing rate and less inter-trial variations. We simulated these physiological phenomena using a neural network model based on spontaneous activity, assuming that the visual attention modulation could be achieved by a change in variance of input firing rate distributed with a lognormal distribution. Consistent with the physiological studies, an increase in firing rate and a decrease in inter-trial variance was simultaneously obtained in the simulation by increasing variance of input firing rate distribution. These results indicate that visual attention forms strong sparse and weak dense input or a ‘winner-take-all’ state, to improve the signal-to-noise ratio of the target information.

Keywords

Visual Attention Neural Network Model Spontaneous Activity Lognormal Distribution 

References

  1. 1.
    Luck, S.J., Chelazzi, L., Hillyard, S.A., Desimone, R.: Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. Journal of Neurophysiology 77(1), 24–42 (1997)Google Scholar
  2. 2.
    Fries, P., Reynolds, J.H., Rorie, A.E., Desimone, R.: Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention. Science 291(5508), 1560–1563 (2001)CrossRefGoogle Scholar
  3. 3.
    Weerd, P.D., Peralta, M.R., Desimone, R., Ungerleider, L.G.: Loss of attentional stimulus selection after extrastriate cortical lesions in macaques. Nature Neuroscience 3(4), 409 (2000)CrossRefGoogle Scholar
  4. 4.
    Reynolds, J.H., Heeger, D.J.: The normalization model of attention. Neuron 61(2), 168–185 (2009)CrossRefGoogle Scholar
  5. 5.
    Mitchell, J.F., Sundberg, K.A., Reynolds, J.H.: Differential attention-dependent response modulation across cell classes in macaque visual area V4. Neuron 55(1), 131–141 (2007)CrossRefGoogle Scholar
  6. 6.
    Churchland, M.M., Yu, B.M., Cunningham, J.P., Sugrue, L.P., Cohen, M.R., Corrado, G.S., Newsome, W.T., Clark, A.M., Hosseini, P., Scott, B.B., Bradley, D.C., Smith, M.A., Kohn, A., Movshon, J.A., Armstrong, K.M., Moore, T., Chang, S.W., Snyder, L.H., Lisberger, S.G., Priebe, N.J., Finn, I.M., Ferster, D., Ryu, S.I., Santhanam, G., Sahani, M., Shenoy, K.V.: Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nature Neuroscience 13(3), 369–378 (2010)CrossRefGoogle Scholar
  7. 7.
    Tolhurst, D.J., Movshon, J.A., Dean, F.: The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Research 23(8), 775–785 (1982)CrossRefGoogle Scholar
  8. 8.
    McAdams, C.J., Maunsell, J.H.: Effects of attention on the reliability of individual neurons in monkey visual cortex. Neuron 23(4), 765–773 (1999)CrossRefGoogle Scholar
  9. 9.
    Brumberg, J.C., Nowak, L.G., McCormick, D.A.: Ionic mechanisms underlying repetitive high-frequency burst firing in supragranular cortical neurons. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience 20(13), 4829–4843 (2000)Google Scholar
  10. 10.
    Nowak, L.G., Azouz, R., Sanchez-Vives, M.V., Gray, C.M., McCormick, D.A.: Electrophysiological classes of cat primary visual cortical neurons in vivo as revealed by quantitative analyses. Journal of Neurophysiology 89(3), 1541–1566 (2003)CrossRefGoogle Scholar
  11. 11.
    Vigneswaran, G., Kraskov, A., Lemon, R.N.: Large identified pyramidal cells in macaque motor and premotor cortex exhibit “thin spikes”: implications for cell type classification. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience 31(40), 14235–14242 (2011)CrossRefGoogle Scholar
  12. 12.
    Teramae, J., Tsubo, Y., Fukai, T.: Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links. Scientific Reports 2, 485 (2012)CrossRefGoogle Scholar
  13. 13.
    Berkes, P., Orbn, G., Lengyel, M., Fiser, J.: Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331(1), 83–87 (2011)Google Scholar
  14. 14.
    Hromádka, T., DeWeese, M., Zador, A.: Sparse representation of sounds in the unanesthetized auditory cortex. PLoS Biology 6(1), e16 (2008)Google Scholar
  15. 15.
    Koulakov, A.A., Hromádka, T., Zador, A.M.: Correlated connectivity and the distribution of firing rates in the neocortex. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience 29(12), 3685–3694 (2009)CrossRefGoogle Scholar
  16. 16.
    Mitchell, J.F., Sundberg, K.A., Reynolds, J.H.: Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 63(6), 879–888 (2009)CrossRefGoogle Scholar
  17. 17.
    Cohen, M.R., Maunsell, J.H.R.: Attention improves performance primarily by reducing interneuronal correlations. Nature Neuroscience 12(12), 1594–1600 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yoshihiro Nagano
    • 1
  • Norifumi Watanabe
    • 2
  • Atsushi Aoyama
    • 1
  1. 1.Faculty of Environment and Information StudiesKeio UniversityKanagawaJapan
  2. 2.School of Computer ScienceTokyo University of TechnologyHachioji-shiJapan

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