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Analysis of higher-order neuronal interactions based on conditional inference.
Gutig R, Aertsen A, Rotter S.
Neurobiology
and Biophysics, Institute of Biology III, Albert-Ludwigs-University,
Schanzlestrasse 1, 79104 Freiburg, Germany.
r.guetig@biologie.hu-berlin.de
Higher-order neural interactions,
i.e., interactions that cannot be reduced to interactions between pairs
of cells, have received increasing attention in the context of recent
attempts to understand the cooperative dynamics in cortical neural
networks. Typically, likelihood-ratio tests of log-linear models are
being employed for statistical inference. The parameter estimation of
these models for simultaneously recorded single-neuron spiking
activities is a crucial ingredient of this approach. Extending a
previous investigation of a two-neuron system, we present here the
general formulation of an exact test suited for the detection of
positive higher-order interactions between m neurons. This procedure
does not require the estimation of any interaction parameters and
additionally optimizes the test power of the statistical inference. We
apply the approach to a three-neuron system and show how second-order
and third-order interactions can be reliably distinguished. We study
the performance of the method as a function of the interaction strength.
PMID: 12750897 [PubMed - indexed for MEDLINE]
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