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Operations research methods for estimating the population size of neuron types

Author

Listed:
  • Sarojini M. Attili

    (George Mason University)

  • Sean T. Mackesey

    (George Mason University)

  • Giorgio A. Ascoli

    (George Mason University)

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 throughout the brain and quantifying the population sizes of distinct classes in different regions is a key subject of research in the neuroscience community. The total number of neurons in the brain has been estimated for multiple species, but 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 computationally 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 towards any neural system, we illustrate its usage on the rodent hippocampus.

Suggested Citation

  • Sarojini M. Attili & Sean T. Mackesey & Giorgio A. Ascoli, 2020. "Operations research methods for estimating the population size of neuron types," Annals of Operations Research, Springer, vol. 289(1), pages 33-50, June.
    • Handle: RePEc:spr:annopr:v:289:y:2020:i:1:d:10.1007_s10479-020-03542-7
    DOI: 10.1007/s10479-020-03542-7
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    References listed on IDEAS

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    1. Adam Tauman Kalai & Santosh Vempala, 2006. "Simulated Annealing for Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 31(2), pages 253-266, May.
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