Abstract

Understanding brain computation requires assembling a complete catalog of its architectural components. Although the brain is organized into several anatomical and functional regions, it is ultimately the neurons in every region that are responsible for cognition and behavior. Thus, classifying neuron types through-out the brain and quantifying the population sizes of distinct classes in different regions is a key subject of research in the neuroscience community. Although the total number of neurons in the brain has been estimated for multiple species, the definition and population size of each neuron type are still open questions even in common model organisms: the so called cell census problem. We propose a methodology that uses operations research principles to estimate the number of neurons in each type based on available information on their distinguishing properties. Thus, assuming a set of neuron type definitions, we provide a solution to the issue of assessing their relative proportions. Specifically, we present a three-step approach that includes literature search, equation generation, and numerical optimization. Solving numerically the set of equations generated by literature mining yields best estimates or most likely ranges for the number of neurons in each type. While this strategy can be applied to any neural system, we illustrate its usage on the rodent hippocampus.