< Back to Article

Analyzing Short-Term Noise Dependencies of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight Transformation

Figure 6

Log likelihoods of the best fitting MVN, Poisson latent variables, and copula-based models for the validation set.

(A) Log likelihoods for the discretized multivariate normal distribution (circles), the multivariate Poisson latent variables distribution (crosses), the best fitting copula-based model with Poisson (squares), and with negative binomial marginals (diamonds). The figure shows the log likelihoods averaged over all different stimuli, but separately for the pre-stimulus, sample stimulus, delay, and test stimulus phase of the memory task. For the best fitting copula, we considered all the copula families shown in B. AMH denotes the Ali-Mikhail-Haq family, FGM the Farlie-Gumbel-Morgenstern family (see Table 1). For the order model of the FGM family we set all but the first parameters to zero, therefore leaving only parameters for pairwise interactions. In contrast, for the order model we set all but the first parameters to zero. (B) Difference between the log likelihood of a model with independent spike-counts and negative binomial marginals (“ind. model”) and the log likelihoods of the best fitting representatives of the different copula-based models shown in the legend. Negative binomial marginals were used. Data was again averaged over the different stimuli.

doi: https://doi.org/10.1371/journal.pcbi.1000577.g006