Basis for temporal filters in the cerebellar granular layer (Roessert et al. 2015)

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Accession:168950
This contains the models, functions and resulting data as used in: Roessert C, Dean P, Porrill J. At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters. It is based on code used for Yamazaki T, Tanaka S (2005) Neural modeling of an internal clock. Neural Comput 17:1032-58
Reference:
1 . Roessert C, Dean P, Porrill J (2015) At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters PLOS Computational Biology [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program;
Model Concept(s): Rate-coding model neurons; Reservoir Computing;
Implementer(s): Roessert, Christian [christian.a at roessert.de];
This contains the models, functions and resulting data as used in:

Roessert C, Dean P, Porrill J. At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters

It is based on code used for Yamazaki T, Tanaka S (2005) Neural modeling of an internal clock. Neural Comput 17:1032-58


1. Recompile if necessary. In ./cells/ifun execute:
python setup.py build_ext --inplace
python setup2.py build_ext --inplace
python setup2b.py build_ext --inplace
python setup2re.py build_ext --inplace

2. Run the simulations:

Data will be saved to ./data
Intermediate figure will be saved to ./figs/dump and ./figs/Pub
Figures will be saved to: ./figs/Pub

- To run a single instance of the one-population network execute:
python Plots_Closedloop.py -o ifun -nompi

- To plot FIGURE 2
python Plots_Closedloop.py -o fig1 -nompi

- To plot FIGURE 3:
python Plots_Closedloop.py -o fig2 -nompi

- To plot FIGURE 4:
python Plots_Closedloop.py -o fig3 -nompi

- To plot FIGURE 5:
python Plots_Closedloop.py -o fig3b -nompi

- To plot FIGURE 6:
python Plots_Closedloop.py -o fig4l -nompi

- To plot FIGURE 7:
python Plots_Closedloop.py -o fig51l -nompi

- To plot FIGURE 8:
python Plots_Closedloop.py -o fig52l -nompi

- To plot FIGURE 9:
python Plots_Closedloop.py -o figRe6 -nompi



Running on a single core takes a very long time, it is suggested to use a cluster.
Here is an example to run on a cluster using Sun Grid Engine:

- To generate a single trace for e.g. Fig 6:
qsub -v J=Plots_Closedloop.py,O=fig4lruntest -pe ompigige 64 -l rmem=32G -l mem=32G PBSinsigneo.sh

- Run a trace with recurrent filter (Fig 9):
qsub -v J=Plots_Closedloop.py,O=figRe6runtest -pe ompigige 64 -l rmem=32G -l mem=32G PBSinsigneo.sh


All the code and data files that are used in the publication can be found here:
https://github.com/croessert/ClosedLoopRoessertEtAl 


** Terms of use:

This code, "ClosedLoopRoessertEtAl", is a derivative of "internalclock" by Takeru Honda and Tadashi Yamazaki 
used under CC-BY (http://creativecommons.org/licenses/by/3.0/). 
The code of "internalclock" was downloaded from https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=115966
"ClosedLoopRoessertEtAl" is licensed under CC BY by Christian Rössert (christian.a [4t] roessert.de).