I thought this seemed obvious - my bad.
The paths are stored in an array just like any other data structure in the program.
I am using random walks with direction bias to generate the paths now.
I am thinking of adding a branch feature where after a certain size run I start a new run from a prior generated node selected at random. This would change the sampling density vs distance from the cell body.
You can get fancy with this.
One Rule to Grow Them All: A General Theory of Neuronal Branching and Its Practical Application
http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000877&type=printable
Each dendrite has a random selection of the generated paths, fixed at the time of map creation.
The reason for multiple paths is that a single fixed pattern has aliasing issues - like a Moiré pattern.
There is a surprising amount of literature (to me anyway) that discusses dendrite shapes and how they get that way.
The single dendritic branch as a fundamental functional unit in the nervous system
https://static1.squarespace.com/static/5267aed6e4b03cb52f5e0a7b/t/52704e46e4b09a993f7fa02c/1383091782768/BrancoHäusser_CON2010.pdf
Generation, description, and storage of dendritic morphology data
Assisted morphogenesis: glial control of dendrite shapes
Conserved properties of dendritic trees in four cortical interneuron subtypes
https://www.nature.com/articles/srep00089
Check out the reference list on this link:
Modelling Dendrite Shape from Wiring Principles
There is a lot going on inside the dendrite - it’s not just a passive wire. I continue my studies to see if any of this aids in learning patterns. An example:
Dendritic geometry shapes neuronal cAMP signaling to the nucleus
https://www.nature.com/articles/ncomms7319