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Can Spike Coordination Be Differentiated from Rate Covariation?

staude{at}brain.riken.jp Computational Neuroscience Group, RIKEN Brain Science Institute, Wako-Shi 351-0198, Japan

Stefan Rotter

stefan.rotter{at}biologie.uni-freiburg.de Institute for Frontier Areas of Psychology and Mental Health, 79098 Freiburg, Germany, and Bernstein Center for Computational Neuroscience, 79104 Freiburg, Germany

Sonja Grüun

gruen{at}brain.riken.jp Computational Neuroscience Group, RIKEN Brain Science Institute, Wako-Shi 351-0198, Japan, and Bernstein Center for Computational Neuroscience, 10099 Berlin, Germany

There has been a long and lively debate on whether rate covariance and temporal coordination of spikes, regarded as potential origins for correlations in cortical spike signals, fulfill different roles in the cortical code. In this context, studies that report spike coordination have often been criticized for ignoring fast nonstationarities, which would result in wrongly assigned spike coordination. The underlying hypothesis of this critique is that spike coordination is essentially identical to rate covariation, only on a shorter timescale. This study investigates the validity of this critique. We provide a decomposition for the cross-correlation function of doubly stochastic point processes, where each of the components corresponds precisely to the concepts of dependence under investigation. This allows us to correct the correlation function for rate effects, which implies that spike coordination and rate covariation are statistically separable concepts of dependence. Furthermore, we present direct and intuitive model implementations of the discussed concepts and illustrate that their difference is not a matter of timescale. Analysis of data generated by our models and analytical description of the relevant estimators reveals, however, that spike coordination dramatically influences the accuracy of rate covariance estimation. As a consequence, extreme parameter combinations can lead to situations where the concept of dependence cannot be identified empirically. However, for a wide range of parameters, the concept of dependence underlying a given data set can be identified regardless of its timescale.