Resumen

ALDANA, Maximino. Scaling laws and criticality in voter models and neuronal dynamics. Inter disciplina [online]. 2020, vol.8, n.20, pp.23-54.  Epub 14-Ago-2020. ISSN 2448-5705.  https://doi.org/10.22201/ceiich.24485705e.2020.20.71191.

An important property of many complex systems is the presence of scaling laws, which are characterized by the fact that some of the variables that describe the behavior of the system are related through power-laws. Scaling laws have two important general implications: (i) the self-similarity of the system over a wide range of scales (each part of the system is similar to the entire system), and (ii) “rare events” which should occur with low probability are in fact not so “rare” and occur much more often than expected. In this work we discuss dynamical criticality as one of the main mechanisms generating scaling laws in complex systems. We will focus mainly on majority voter models and neuronal networks. It is surprising that two systems which are apparently so different (networks of voters and networks of neurons) can actually be described using the same conceptual and methodological tools. This illustrates the universality of critical phenomena and the corresponding scaling laws.

Palabras llave : criticality; scaling; neural networks; voting models.

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