Exponential distance distribution of connected neurons in simulations of two-dimensional in vitro neural network development

Abstract

The distribution of the geometric distances of connected neurons is a practical factor underlying neural networks in the brain. It can affect the brain’s dynamic properties at the ground level. Karbowski derived a power-law decay distribution that has not yet been verified by experiment. In this work, we check its validity using simulations with a phenomenological model. Based on the in vitro twodimensional development of neural networks in culture vessels by Ito, we match the synapse number saturation time to obtain suitable parameters for the development process, then determine the distribution of distances between connected neurons under such conditions. Our simulations obtain a clear exponential distribution instead of a power-law one, which indicates that Karbowski’s conclusion is invalid, at least for the case of in vitro neural network development in two-dimensional culture vessels.

Keywords

distance distribution / connected neurons / development / exponential / power-law / neural networks / complex systems

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2017 Higher Education Press and Springer-Verlag Berlin Heidelberg