Figure 3.

Three response relationships among populations of SC neurons that are relevant to the principle of inverse effectiveness. The data were taken from (Stanford et al., 2005), and each dot represents a sample from a different stimulus or neuron. Left: A correlation exists between the estimated response variance and the response magnitude across the population, with the line of unity (solid black) providing the predicted relationship for a Poisson process. Although the data are roughly consistent with this model, the Poisson model does more poorly at high response magnitudes where the model’s assumptions become less well justified. Center: There is a tight correlation (r²=0.87) between the magnitudes of the multisensory and the best unisensory responses and the inset shows a log-log plot of this trend. Right: A characteristic example of “inverse effectiveness” showing a negative trend between MSI and the magnitude of the best unisensory response (inset shows the log-log plot). MSI = multisensory index; Imp. = impulses.