4.4 Results: Application to Hippocampal CA3-CA1 Population Activity 115 are smoothed to continuous signals y g and y g through convolution with a ^ Gaussian kernel having standard deviation g . Correlation coefficients r are then calculated as T T T r( g ) = t=0 y g (t)y g (t) ^ t=0 y g (t)y g (t) t=0 y g (t)^ g (t) ^ y (4.29) Because y g and y g are both positive vectors, r is a quantity between ^ 0 and 1 that measures the similarity between y and y as a function of the ^ "smoothness parameter" g · g essentially determines the temporal resolution used in comparing the predicted spike train with the actual spike train. A large value means low temporal resolution; a small value means high temporal resolution. This parameter does not influence model estimation because the estimation is carried out by maximizing the likelihood function defined with a fixed 2-ms bin size. In this study, g varies from 2 to 100 ms. The mean and standard deviation of r is estimated with 32 trials of simulation.