NeuroMac (Neuronal Morphologies and Circuits) is a computational framework to generate large amounts of virtual neuronal morphologies simultaneously, as well as their resultants microcircuits.

Neurons develop in a densely packed brain substrate inhabited by other neurone, glia cells, vasculature etc. From experimental work is is clear that microscopic interactions between a developing neuron and the environment influence the adult morphology of that neuron.

NeuroMaC is based on this key experimental finding and allows users to generate virtual morphologies based on phenomenological growth-rules. In short, a virtual morphologies is grown by repeatedly simulated growth-cones. Each growth-cone can contain its own growth rules and these growth rules can be context-dependent (e.g., depending on the location in the substrate) and can be based on interactions with other entities int he substrate (e.g., repulsion and attraction). These rules are phenomenological representations of the true biological processes because we do not simulate any biochemical or physical process.

We developed a prototype of this framework and validated its capability to generate dendritic morphologies of various cells types. These initial findings are published in the following article:

Ben Torben-Nielsen & Erik De Schutter. Context-aware modelling of neuronal morphologies. Frontiers in neuroanatomy (2014, in press)

The Python prototype software used for this publication is available on github.

Rudimentary documentation can be found here

For more information contact Ben Torben-Nielsen.