Adaptive dual control of deep brain stimulation in Parkinsons disease simulations (Grado et al 2018)

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Accession:247310

Reference:
1 . Grado LL, Johnson MD, Netoff TI (2018) Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson's disease. PLoS Comput Biol 14:e1006606 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neural mass;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python;
Model Concept(s): Parkinson's;
Implementer(s):
/
MFM_BayesADC_ModelDB
.git
hooks
applypatch-msg.sample *
commit-msg.sample *
fsmonitor-watchman.sample *
post-update.sample *
pre-applypatch.sample *
pre-commit.sample *
prepare-commit-msg.sample *
pre-push.sample *
pre-rebase.sample *
pre-receive.sample *
update.sample *
                            
#!/bin/sh
#
# An example hook script to make use of push options.
# The example simply echoes all push options that start with 'echoback='
# and rejects all pushes when the "reject" push option is used.
#
# To enable this hook, rename this file to "pre-receive".

if test -n "$GIT_PUSH_OPTION_COUNT"
then
	i=0
	while test "$i" -lt "$GIT_PUSH_OPTION_COUNT"
	do
		eval "value=\$GIT_PUSH_OPTION_$i"
		case "$value" in
		echoback=*)
			echo "echo from the pre-receive-hook: ${value#*=}" >&2
			;;
		reject)
			exit 1
		esac
		i=$((i + 1))
	done
fi