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Modelling the effects of short and random proto-neural elongations (de Wiljes et al 2017)

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Accession:231859
"To understand how neurons and nervous systems first evolved, we need an account of the origins of neural elongations: why did neural elongations (axons and dendrites) first originate, such that they could become the central component of both neurons and nervous systems? Two contrasting conceptual accounts provide different answers to this question. Braitenberg's vehicles provide the iconic illustration of the dominant input-output (IO) view. Here, the basic role of neural elongations is to connect sensors to effectors, both situated at different positions within the body. For this function, neural elongations are thought of as comparatively long and specific connections, which require an articulated body involving substantial developmental processes to build. Internal coordination (IC) models stress a different function for early nervous systems. Here, the coordination of activity across extended parts of a multicellular body is held central, in particular, for the contractions of (muscle) tissue. An IC perspective allows the hypothesis that the earliest proto-neural elongations could have been functional even when they were initially simple, short and random connections, as long as they enhanced the patterning of contractile activity across a multicellular surface. The present computational study provides a proof of concept that such short and random neural elongations can play this role. ..."
Reference:
1 . de Wiljes OO, van Elburg RAJ, Keijzer FA (2017) Modelling the effects of short and random proto-neural elongations. J R Soc Interface [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Brian 2; Python;
Model Concept(s): Early evolution;
Implementer(s): de Wiljes, O. O. [otdewiljes at gmail.com]; 
*How to use this code*

The experiments are run using SkinBrian.py, a framework for running experiments of cells in simple topologies. As of yet, only the tubular topology is implemented and only the integrate-and-fire cell model. 

It allows for easy scanning of parameter spaces; the code below <if __name__ == '__main__':> is fairly self-explanatory. Simply create your own set of model parameters, make sure the model object takes that set as input, and press F5! Also note that there are a few global variables which may impact your performance. Debugging works best on just 1 core, then the multiprocessing module is not called.

There is a caveat when it comes to using one output directory more than once: there is a built-in method to help when very long runs with many parameter combinations break down (as they inevitably will), which will continue where the runs broke down. This goes by run index; this may be different when other parameters are set, so make sure to clear out the output directory if the parameter space and model code is NOT _exactly_ the same.

All other files are for analysis and visualization. They require you to set the correct directory for the output to look into. stripeAnalysis.py is the main file for overall quantitative analysis; SkinBrianVectorViz.py is the main file for detailed, single-run analysis. Both are called in MikadoPaperVizScript.py upon which the analyses in the 'Short and Random'-paper are based.

It requires Python 2.7 and Brian2. All other dependencies are also dependencies of Brian2.

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