Religiosity is one of the most important sociological aspects of populations. All religions may evolve in their beliefs and adapt to the society developments. A religion is a social variable, like a language or wealth, to be studied like any other organizational parameter. Several questions can be raised, as considered in this study; e.g.: i) From a macroscopic point of view: How many religions exist at a given time? ii) From a microscopic viewpoint: How many adherents belong to one religion? Does the number of adherents increase or not, and how? No need to say that if quantitative answers and mathematical laws are found, agent-based models can be imagined to describe such non-equilibrium processes. It is found that empirical laws can be deduced and related to preferential attachment processes, like on an evolving network; we propose two different algorithmic models reproducing as well the data. Moreover, a population growth-death equation is shown to be a plausible modeling of evolution dynamics in a continuous-time framework. Differences with language dynamic competition are emphasized.

The present study asks in what way the activity of a neuronal population responding to a sensory stimulus could be most efficiently decoded, or ‘read off’, by the target neurons. A simple solution to this problem has been proposed – pooling the activity of responding neurons. However, pooling can be inefficient if sensory information is encoded by the ‘label’ of each neuron firing a spike. We have tested the efficiency of pooling by quantifying the extent to which information about a sensory stimulus is diminished when the identity of the individual neurons is lost by pooling. Analyzing the response of small groups of neurons in rat barrel cortex to single-whisker deflection, we found that pooling neurons within the same column is efficient for representing stimulus position; it causes a loss of only 1% of the information about whether the principal whisker was stimulated, and a loss of 5–12% of the finer information about which of nine possible whiskers (the principal and its neighbors) was stimulated. Cross-column pooling led to larger information losses, in the range of 25–55%. Thus, to decode stimulus position from the discharge of barrel cortex populations, ‘downstream’ neurons could pool the activity arising from neurons of the same column, while maintaining the activity arising from neurons of separate columns at least partially segregated. Since such parcellation is present in some of the projections from barrel cortex, these findings suggest that columnar organization of barrel cortex serves to facilitate decoding of the location of the stimulated whisker.