We think that to fully understand neuronal morphologies, circuits and their variance we have to look at the substrate at large. That is, how neurons developed and interactions between a developing neuron and the substrate (e.g., toher neurons, boundaries, capillaries,...) influence their morphology. For this purpose we developed NeuroMaC
NeuroMaC is a phenomenological, computational framework to generate large numbers of virtual neuronal morphologies (and resultant microcircuits) simultaneously according to growth-rules expressed in terms of interactions with the environment.
- NeuroMaC: Neuronal Morphologies & Circuits
- Computational framework: a suite of software tools built around a central concept
- Virtual neuronal morphologies: 3D digital descriptions of neuronal shape (both axons and dendrites)
- Microcircuits: Morphologies are generated together in a simulated volume. With the addition of connections rules circuits emerge.
- Interactions: Growth-cones branch, terminate and elongate. Each of these steps can be influenced by environmental cues. Most obvious is guidance through repulsion and attraction to simulated cues.
- Phenomenological: Neither biochemical pathways nor physics are simulated. As such, growth is purely phenomenological. NeuroMaC is not a simulator of actual neuronal development.
Warning
Currenly, a prototype of NeuroMaC is implemented in Python. This version is a proof-of-principle and nothing beyond that. This prototype has many limitations and we are working towards a non-prototype version, which should be released in the next year. The current prototype code is freely available.
Main author: Ben Torben-Nielsen