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Single-Unit Recordings Revisited: Activity in Recurrent Microcircuits

  • Raul C. Mureşan
  • Gordon Pipa
  • Diek W. Wheeler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

We investigated the relevance of single-unit recordings in the context of dynamical neural systems with recurrent synapses. The present study focuses on modeling a relatively small, biologically-plausible network of neurons. In the absence of any input, the network activity is self-sustained due to the resonating properties of the neurons. Recording of single units reveals an increasingly complex response to stimulation as one proceeds higher into the processing stream hierarchy. Results suggest that classical analysis methods, using rate and averaging over trials, fail to describe the dynamics of the system, and instead hide the relevant information embedded in the complex states of the network. We conclude that single-unit recordings, which are still extensively used in experimental neuroscience, need to be more carefully interpreted.

Keywords

Firing Rate Subthreshold Oscillation Experimental Neuroscience Classical Analysis Method Multiunit Recording 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Dayan, P., Abbott, L.F.: Theoretical Neuroscience. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  2. 2.
    Fellous, J.M., Houweling, A.R., Modi, R.H., Rao, R.P.N., Tiesinga, P.H.E., Sejnowski, T.J.: Frequency dependence of spike timing reliability in cortical pyramidal cells and in-terneurons. J. Neurophysiol. 85, 1782–1787 (2001)Google Scholar
  3. 3.
    Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, New York (2002)zbMATHGoogle Scholar
  4. 4.
    Izhikevich, E.M.: Resonate-and-Fire Neurons. Neural Networks 14, 883–894 (2001)CrossRefGoogle Scholar
  5. 5.
    Izhikevich, E.M.: Resonance and Selective Communication Via Bursts in Neurons Having Subthreshold Oscillations. BioSystems 67, 95–102 (2002)CrossRefGoogle Scholar
  6. 6.
    Izhikevich, E.M.: Simple Model of Spiking Neurons. IEEE Transactions on Neural Networks 14, 1569–1572 (2003)CrossRefGoogle Scholar
  7. 7.
    Maass, W., Natschläger, T., Markram, H.: Computational models for generic cortical microcircuits. In: Feng, J. (ed.) Computational Neuroscience: A Comprehensive Approach, ch. 18, pp. 575–605. Chapman & Hall/CRC, Boca Raton (2004)Google Scholar
  8. 8.
    Natschläger T., Maass W., Zador A. Efficient temporal processing with biologically realistic dynamic synapses. Network: Computation in Neural Systems, 2001, 12:75-87. zbMATHGoogle Scholar
  9. 9.
    Singer, W.: Response synchronization: a universal coding strategy for the definition of relations. In: Gazzaniga, M.S. (ed.) The Cognitive Neurosciences. MIT Press, Cambridge (1999)Google Scholar
  10. 10.
    Super, H., Roelfsema, P.: Chronic multiunit recordings in behaving animals: advantages and limitations. Progress in Brain Research 147, 263–281 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Raul C. Mureşan
    • 1
    • 2
    • 3
    • 4
  • Gordon Pipa
    • 1
    • 2
  • Diek W. Wheeler
    • 1
    • 2
  1. 1.Frankfurt Institute for Advanced StudiesFrankfurt a.M.Germany
  2. 2.Max Planck Institute for Brain ResearchFrankfurt a.M.Germany
  3. 3.Center for Cognitive and Neural Studies (Coneural)Cluj-NapocaRomania
  4. 4.Technical University of Cluj-NapocaCluj-NapocaRomania

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