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 . Rössert 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 Comput Biol 11:e1004515 [PubMed]
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];
#!/bin/bash

## find . -maxdepth 1 -name '*ifun2re*' -delete
## qstat -q insigneo.q
## qrsh -q insigneo.q -P insigneo
## qstat -F | grep insigneo
## qstat | grep insigneo

## submit job:
## qsub -v J=Plots_Closedloop.py,O=ifun -pe ompigige 1 -l rmem=32G -l mem=32G PBSinsigneo.sh
## qsub -v J=Plots_Closedloop.py,O=fig4lruntest -pe ompigige 64 -l rmem=32G -l mem=32G PBSinsigneo.sh

##$ -l h_rt=1:00:00
##$ -l mem=128G
##$ -l rmem=64G

### Queue name
#$ -q insigneo.q
#$ -P insigneo

#$ -l arch=intel*

### Output files
#$ -e log/
#$ -o log/

#$ -j y

module load compilers/intel/12.1.15
module load mpi/intel/openmpi/1.6.4

# Run
echo "======================================================================="

echo $J
echo $O
mpirun python $J -o $O > log/$O.log2 2>&1

echo "======================================================================="

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