Modeling and analysis of axonogenesis: Random spatial network perspective

Data from time lapse microscopy of live embryonic rat hippocampal neurons growing in cell culture are used to study the dynamics of axonal growth.1 We analyze axonal trajectory data based on cells growing in a homogeneous medium. Due to the noisy nature of the data we develop filtering algorithms to smoothen out the paths while maintaining the underlying dynamics of the axon growth process. We analyze the new paths and propose a model for growth cone kinematics during axonogenesis without a gradient field. In this work we present a simple renewal process with the aim of reproducing certain path behaviors of the growth cone. Future development will include, renewal process simulation, and gradients eects.