Speaker
Description
Neurons receive information through their dendrites. During development, dendrites grow, retract, and branch as they search for connections, and the resulting dendritic arbor shapes the structure of the broader neural network. This talk introduces a flexible model describing the full spatial structure of a single neuron through three processes: a lifetime process that determines whether each dendrite is growing or retracting; a growth process describing dendritic extension in space; and a branching process that controls the generation of new branches. Crucially, retraction and branching make it necessary to track entire dendritic paths rather than only their endpoints. While this is handled implicitly in many existing simulations, we present here an explicit construction of a path-valued stochastic process for modelling dendrites. Numerical simulations demonstrate that the model can adapt to a range of developmental environments.
