Abstract:
It is well known that the connectivity of a diverse range of nervous systems is topologically complex, being neither completely random nor regular like a lattice. Insights into the mechanisms that give rise to this complexity can be obtained through generative network models. These models can be used to simulate network growth in silico according to specific wiring rules and/or constraints on connectome development. The properties of the resulting synthetic networks can then be compared to empirical data to identify which rules yield the most realistic networks. In this talk, I will cover some basic concepts and techniques used in generative network modelling, explain how network models have been used to understand different features of connectome organization, and outline limitations of current approaches and avenues for further development.
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