The authors propose an empirical analysis of the current situation in monotowns. The study questions the perceived seriousness of the "monotown problem" as well as the actual challenges it presents. The authors use a cluster analysis to divide monotowns into groups for further structural comparison. The structural differences in the available databases limit the possibilities of empirical analysis. Hence, alternative approaches are required. The authors consider possible reasons for the limitations identified. Special attention is paid to the monotowns that were granted the status of advanced development territories. A comparative analysis makes it possible to study their general characteristics and socioeconomic indicators. The authors apply the theory of opportunistic behaviour to describe potential problems caused by the lack of unified criteria for granting monotowns the status of advanced development territories. The article identifies the main stakeholders and the character of their interaction; it desc ribes a conceptual model built on the principal/agent interactions, and identifies the parametric space of mutually beneficial cooperation. The solution to the principal/agent problem suggested in the article contributes to the development of an alternative approach to the current situation and a rational approach to overcoming the "monotown problem".
The true revolution in the age of digital neuroanatomy is the ability to extensively quantify anatomical structures and thus investigate structure-function relationships in great detail. Large-scale projects were recently launched with the aim of providing infrastructure for brain simulations. These projects will increase the need for a precise understanding of brain structure, e.g., through statistical analysis and models. From articles in this Research Topic, we identify three main themes that clearly illustrate how new quantitative approaches are helping advance our understanding of neural structure and function. First, new approaches to reconstruct neurons and circuits from empirical data are aiding neuroanatomical mapping. Second, methods are introduced to improve understanding of the underlying principles of organization. Third, by combining existing knowledge from lower levels of organization, models can be used to make testable predictions about a higher-level organization where knowledge is absent or poor. This latter approach is useful for examining statistical properties of specific network connectivity when current experimental methods have not yet been able to fully reconstruct whole circuits of more than a few hundred neurons.