Biological Cybernetics

, Volume 89, Issue 4, pp 289–302 | Cite as

Partial correlation analysis for the identification of synaptic connections

  • Michael EichlerEmail author
  • Rainer Dahlhaus
  • Jürgen Sandkühler


In this paper, we investigate the use of partial correlation analysis for the identification of functional neural connectivity from simultaneously recorded neural spike trains. Partial correlation analysis allows one to distinguish between direct and indirect connectivities by removing the portion of the relationship between two neural spike trains that can be attributed to linear relationships with recorded spike trains from other neurons. As an alternative to the common frequency domain approach based on the partial spectral coherence we propose a new statistic in the time domain. The new scaled partial covariance density provides additional information on the direction and the type, excitatory or inhibitory, of the connectivities. In simulation studies, we investigated the power and limitations of the new statistic. The simulations show that the detectability of various connectivity patterns depends on various parameters such as connectivity strength and background activity. In particular, the detectability decreases with the number of neurons included in the analysis and increases with the recording time. Further, we show that the method can also be used to detect multiple direct connectivities between two neurons. Finally, the methods of this paper are illustrated by an application to neurophysiological data from spinal dorsal horn neurons.


Dorsal Horn Spike Train Spinal Dorsal Spinal Dorsal Horn Connectivity Pattern 
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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The authors wish to thank an anonymous referee for his helpful comments on an earlier version of this paper.

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Michael Eichler
    • 1
    Email author
  • Rainer Dahlhaus
    • 1
  • Jürgen Sandkühler
    • 2
  1. 1.Institut für Angewandte MathematikUniversität HeidelbergHeidelbergGermany
  2. 2.Brain Research InstituteVienna University Medical SchoolViennaAustria

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