TY - JOUR
T1 - Identification of patterns of neuronal connectivity - partial spectra, partial coherence, and neuronal interactions
AU - Rosenberg, J.R.
AU - Halliday, D.M.
AU - Breeze, P.
AU - Conway, B.A.
PY - 1998/8/31
Y1 - 1998/8/31
N2 - The cross-correlation histogram has provided the primary tool for inferring the structure of common inputs to pairs of neurones. While this technique has produced useful results it not clear how it may be extended to complex networks. In this report we introduce a linear model for point process systems. The finite Fourier transform of this model leads to a regression type analysis of the relations between spike trains. An advantage of this approach is that the full range of techniques for multivariate regression analyses becomes available for spike train analysis. The two main parameters used for the identification of neural networks are the coherence and partial coherences. The coherence defines a bounded measure of association between two spike trains and plays the role of a squared correlation coefficient defined at each frequency λ. The partial coherences, analogous to the partial correlations of multiple regression analysis, allow an assessment of how any number of putative input processes may influence the relation between any two output processes. In many cases analytic solutions may be found for coherences and partial coherences for simple neural networks, and in combination with simulations may be used to test hypotheses concerning proposed networks inferred from spike train analyses.
AB - The cross-correlation histogram has provided the primary tool for inferring the structure of common inputs to pairs of neurones. While this technique has produced useful results it not clear how it may be extended to complex networks. In this report we introduce a linear model for point process systems. The finite Fourier transform of this model leads to a regression type analysis of the relations between spike trains. An advantage of this approach is that the full range of techniques for multivariate regression analyses becomes available for spike train analysis. The two main parameters used for the identification of neural networks are the coherence and partial coherences. The coherence defines a bounded measure of association between two spike trains and plays the role of a squared correlation coefficient defined at each frequency λ. The partial coherences, analogous to the partial correlations of multiple regression analysis, allow an assessment of how any number of putative input processes may influence the relation between any two output processes. In many cases analytic solutions may be found for coherences and partial coherences for simple neural networks, and in combination with simulations may be used to test hypotheses concerning proposed networks inferred from spike train analyses.
KW - coherence
KW - finite Fourier transform
KW - linear models
KW - neural networks
KW - partial coherence
KW - point process systems
UR - http://www.scopus.com/inward/record.url?scp=0032585013&partnerID=8YFLogxK
UR - https://www.sciencedirect.com/journal/journal-of-neuroscience-methods
U2 - 10.1016/S0165-0270(98)00061-2
DO - 10.1016/S0165-0270(98)00061-2
M3 - Article
C2 - 9765051
AN - SCOPUS:0032585013
SN - 0165-0270
VL - 83
SP - 57
EP - 72
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 1
ER -