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Experimental studies in cognitive science typically focus on the population average effect. An alternative is to test each individual participant and then quantify the proportion of the population that would show the effect: the prevalence, or participant replication probability. We argue that this approach has conceptual and practical advantages. [ABSTRACT FROM AUTHOR]

Abstract: Mutual information is a principled non-linear measure of dependence between stochastic variables, which is widely used to study the selectivity of neural responses to external stimuli. Here we define and develop a set of novel statistical independence tests based on mutual information, which quantify the significance of neural selectivity to either single features or to multiple, potentially correlated stimulus features like those often present in naturalistic stimuli. If the values of different features are correlated during stimulus presentation, it is difficult to establish if one feature is genuinely encoded by the response, or if it instead appears to be encoded only as a side effect of its correlation with another genuinely represented feature. Our tests provide a way to disambiguate between these two possibilities. We use realistic simulations of neural responses tuned to one or more correlated stimulus features to investigate how limited sampling bias correction procedures affect the statistical power of such independence tests, and we characterize the regimes in which the distribution of information values under the null hypothesis can be approximated by simple distributions (Chi-square or Gaussian). Finally, we apply these tests to experimental data to determine the significance of tuning of the band limited power (BLP) of the gamma [30–100Hz] frequency range of the primary visual cortical local field potential to multiple correlated features during presentation of naturalistic movies. We show that gamma BLP carries significant, genuine information about orientation, space contrast and time contrast, despite the strong correlations between these features. [Copyright &y& Elsevier]

Abstract: Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limited sampling bias which affects the calculation of information from a limited amount of experimental data. We then discuss how to quantify the contribution of individual members of the population, or the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains. [Copyright &y& Elsevier]