Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Neural Networks

ICANN 2018: Artificial Neural Networks and Machine Learning – ICANN 2018 pp 641–651Cite as

State-Space Analysis of an Ising Model Reveals Contributions of Pairwise Interactions to Sparseness, Fluctuation, and Stimulus Coding of Monkey V1 Neurons

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11141))

Abstract

In this study, we analyzed the activity of monkey V1 neurons responding to grating stimuli of different orientations using inference methods for a time-dependent Ising model. The method provides optimal estimation of time-dependent neural interactions with credible intervals according to the sequential Bayes estimation algorithm. Furthermore, it allows us to trace dynamics of macroscopic network properties such as entropy, sparseness, and fluctuation. Here we report that, in all examined stimulus conditions, pairwise interactions contribute to increasing sparseness and fluctuation. We then demonstrate that the orientation of the grating stimulus is in part encoded in the pairwise interactions of the neural populations. These results demonstrate the utility of the state-space Ising model in assessing contributions of neural interactions during stimulus processing.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Aertsen, A.M., Gerstein, G.L., Habib, M.K., Palm, G.: Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J. Neurophysiol. 61(5), 900–917 (1989)

    Article  Google Scholar 

  2. Barlow, H.B.: Possible Principles Underlying the Transformations of Sensory Messages. Oxford University Press, Cambridge (1961)

    Google Scholar 

  3. Cavanaugh, J.R., Bair, W., Movshon, J.A.: Nature and interaction of signals from the receptive field center and surround in macaque v1 neurons. J. Neurophysiol. 88(5), 2530–2546 (2002)

    Article  Google Scholar 

  4. Donner, C., Obermayer, K., Shimazaki, H.: Approximate inference for time-varying interactions and macroscopic dynamics of neural populations. PLoS Comput. Biol. 13(1), e1005309 (2017)

    Article  Google Scholar 

  5. Ganmor, E., Segev, R., Schneidman, E.: Sparse low-order interaction network underlies a highly correlated and learnable neural population code. Proc. Natl. Acad. Sci. USA 108(23), 9679–9684 (2011)

    Article  Google Scholar 

  6. Kohn, A., Smith, M.: Utah array extracellular recordings of spontaneous and visually evoked activity from anesthetized macaque primary visual cortex (v1). CRCNS.org https://doi.org/10.6080/K0NC5Z4X (2016)

  7. Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: a strategy employed by v1? Vis. Res. 37(23), 3311–3325 (1997)

    Article  Google Scholar 

  8. Schneidman, E., Berry, M.J., Segev, R., Bialek, W.: Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440(7087), 1007–1012 (2006)

    Article  Google Scholar 

  9. Shimazaki, H., Amari, S.I., Brown, E.N., Grün, S.: State-space analysis on time-varying correlations in parallel spike sequences. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009 (2009)

    Google Scholar 

  10. Shimazaki, H., Amari, S.I., Brown, E.N., Grün, S.: State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data. PLoS Comput. Biol. 8(3), e1002385 (2012)

    Article  MathSciNet  Google Scholar 

  11. Shoham, S., Fellows, M.R., Normann, R.A.: Robust, automatic spike sorting using mixtures of multivariate t-distributions. J. Neurosci. Methods 127(2), 111–122 (2003)

    Article  Google Scholar 

  12. Smith, M.A., Kohn, A.: Spatial and temporal scales of neuronal correlation in primary visual cortex. J. Neurosci. 28(48), 12591–12603 (2008)

    Article  Google Scholar 

  13. Thorpe, S.J., Fabre-Thorpe, M.: Seeking categories in the brain. Science 291(5502), 260–263 (2001)

    Article  Google Scholar 

  14. Tkac̆ik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., Berry, M.J.: Searching for collective behavior in a large network of sensory neurons. PLoS Comput. Biol. 10(1), e1003408 (2014)

    Google Scholar 

  15. Vaadia, E., et al.: Dynamics of neuronal interactions in monkey cortex in relation to behavioral events. Nature 373(6514), 515–518 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Polytechnique Montreal, Montreal, QC, Canada

    Jimmy Gaudreault

  2. Graduate School of Informatics, Kyoto University, Kyoto, Japan

    Hideaki Shimazaki

  3. Honda Research Institute Japan, Wako, Japan

    Hideaki Shimazaki

Authors
  1. Jimmy Gaudreault
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hideaki Shimazaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hideaki Shimazaki .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gaudreault, J., Shimazaki, H. (2018). State-Space Analysis of an Ising Model Reveals Contributions of Pairwise Interactions to Sparseness, Fluctuation, and Stimulus Coding of Monkey V1 Neurons. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01424-7_63

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01423-0

  • Online ISBN: 978-3-030-01424-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation