Abstract 1358 Scaling relationships in animal nervous systems

Scaling relationships are functions of the form Y = aXb, where Y is a response variable, "a" is a normalization constant, X is a predictor variable (usually body mass) and "b" is an exponent. These functions describe scale-invariant, often universal, behavior and underlying features associated with the complex structure and functioning of biological systems. In this study, we explore the scaling relationships between the number of neurons and glial cells in the nervous systems of animals. We compiled data from the literature for different taxa, encompassing both decentralized and centralized (with brain) nervous systems and implemented simple linear regression analysis to fit power-law relationships (in logarithmic scale). Although we found varying exponent values for different taxa, through appropriate scaling, we were able to collapse data across all groups. Our findings reveal universality in the structure and functioning of the nervous systems of animals. This discovery could fuel theoretical development and enhance our understanding of the origin of this conserved pattern. We acknowledge support from the National Science Foundation Grant Award 2133863 URoL:EN: Towards a unified theory of regulatory functions and networks across biological and social systems. We also acknowledge support from Santa Fe Institute.