Skip to main content

Topographic Modulations of Neural Oscillations in Spiking Networks

  • Conference paper
  • First Online:
  • 1726 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

Abstract

We present a computational model evoked by electrosensory system which is able to display oscillatory activity, and focus on the coherence of the spectral power of the ELL neurons with the topographic modulations for different spatial scale regimes. Numerical simulations reveal that the spatial scale is a very important determinant of neural oscillations in gamma band. The spectral power is enhanced by decreasing feedback spatial spread. This enhancement can also occur if the feedforward is global. However, when the feedforward is topographic, the oscillations saturate to a steady state. In brief, the topographic feedback alone enables the system to modulate gamma activity with the spatial scale, while the introduction of topography in feedforward brings little effect on oscillations. What our results further indicate is that the topographic feedback can induce and enhance oscillations even when the external stimulus is local.

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

Buying options

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. Maler, L.: Neural strategies for optimal processing of sensory signals. Prog. Brain Res. 165, 135–154 (2007)

    Article  Google Scholar 

  2. Chacron, M.J., Bastian, J.: Population coding by electrosensory neurons. J. Neurophysiol. 99, 1825–1835 (2008)

    Article  Google Scholar 

  3. Marsat, G., Longtin, A., Maler, L.: Cellular and circuit properties supporting different sensory coding strategies in electric fish and other systems. Curr. Opin. Neurobiol. 22(4), 686–692 (2012)

    Article  Google Scholar 

  4. Battaglia, D., Brunel, N., Hansel, D.: Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation. Phys. Rev. Lett. 99(23), 238106 (2007)

    Article  Google Scholar 

  5. Hutt, A., Sutherland, C., Longtin, A.: Driving neural oscillations with correlated spatial input and topographic feedback. Phys. Rev. E 78, 021911 (2008)

    Article  MathSciNet  Google Scholar 

  6. Rothma, J.S., Cathala, L., Steuber, V., Silver, R.A.: Synaptic depression enables neuronal gain control. Nature 457(7232), 1015–1018 (2009)

    Article  Google Scholar 

  7. Ly, C., Doiron, B.: Divisive gain modulation with dynamic stimuli in integrate-and-fire neurons. PLoS Comput. Biol. 5(4), e1000365 (2009)

    Article  MathSciNet  Google Scholar 

  8. Serrano, E., Nowotny, T., Levi, R., Smith, B.H., Huerta, R.: Gain control network conditionsin early sensory coding. PLoS Comput. Biol. 9, 7 (2013)

    Article  MathSciNet  Google Scholar 

  9. Mejias, J.F., Payeur, A., Selin, E., Maler, L., Longtin, A.: Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays. Front. Comput. Neurosci. 25, 8–19 (2014)

    Google Scholar 

  10. Doiron, B., Linder, B., Longtin, A., Maler, L., Bastian, J.: Oscillatory activity in electrosensory neurons increases with the spatial correlation of the stochastic input stimulus. Phys. Rev. Lett. 93(4), 048101 (2004)

    Article  Google Scholar 

  11. Lindner, B., Doiron, B., Longtin, A.: Theory of oscillatory firing induced by spatially correlated noise and delayed inhibitory feedback. Phys. Rev. E 72(6), 061919 (2005)

    Article  MathSciNet  Google Scholar 

  12. Marinazzo, D., Kappen, H.J., Gielen, S.C.A.M.: Input-driven oscillations in networks with excitatory and inhibitory neurons with dynamic synapses. Neural Comput. 19, 1739–1765 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  13. Xie, J.L., Wang, Z.J., Longtin, A.: Correlated firing and oscillationsin spiking networks with global delayed inhibition. Neurocomput. 83, 146–157 (2012)

    Article  Google Scholar 

  14. Hansel, D., Sompolinsky, H.: Synchrony and computation in a chaotic neural network. Phys. Rev. Lett. 68, 718–721 (1992)

    Article  Google Scholar 

  15. Roxin, A., Brunel, N., Hansel, D.: Role of delays in shaping spatiotemporal dynamics of neuronal activity in large networks. Phys. Rev. Lett. 94, 238103 (2005)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61203375, and the Doctoral Foundation of University of Jinan under Grant No. XBS1240.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jinli Xie .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Xie, J., Zhao, J., Zhao, Q. (2015). Topographic Modulations of Neural Oscillations in Spiking Networks. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22180-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics