Theoretical and Computational Neuroscience
Author: Carlos Eduardo Valencia Urbina | email: carlos.valencia@ib.edu.ar
Carlos Eduardo Valencia Urbina 1°, Sergia A. Cannas 2°, Pablo M. Gleiser 3°
1° 1 Departmento de Fisica Medica, Centro Atómico Bariloche, CNEA, CONICET
2° Instituto de F??sica Enrique Gaviola de Córdoba, CONICET-UNC
3° Instituto Tecnológico de Buenos Aires
Understanding the relationship between brain architecture and function is a central question in neuroscience. With this goal in mind, many studies have focused on animal models with small nervous systems, such as the worm Caenorhabditis elegans (C. elegans). This is the first organism for which its connectome, i.e., its neurons and how they are connected at the synaptic level, is known. This allows an abstraction of the neuronal system into a set of weighted nodes and links, enabling the development of a theoretical framework for studying the general principles of organization of neuronal structures. Furthermore, it establishes the first step in the study of the relationship between network structure and function, i.e., on the dynamic processes that enable these structures. In this work we analyze experimental data on the neuronal dynamics of C. elegans. We study these time series as point processes, reducing the continuous signals, observing only the maximum values of the neuronal dynamics signals. We find that the distribution of times between maxima presents a broad distribution, with a slow decay. Using information from the worm connectome, we performed numerical simulations of the neural dynamics using a model proposed by Haimovici et al. (Physical Review Letters, 110:178101, 2013). In this model, each node in the network has a three-state variable associated with it, corresponding to a quiescent, excited, or refractory state. Using synchronized cluster size information.