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pgleesonpgleeson
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Add granule cell & more tests
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‎.github/workflows/omv-ci.yml‎

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fail-fast: false
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matrix:
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python-version: [ 3.9 ]
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engine: [ jNeuroML, jNeuroML_NEURON, jNeuroML_validate, jNeuroML_Moose ]
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engine: [ jNeuroML, jNeuroML_NEURON, jNeuroML_validate, jNeuroML_Moose, jNeuroML_EDEN, jNeuroML_NetPyNE ]
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steps:
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- uses: actions/checkout@v4

‎.gitignore‎

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x86_64
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arm64
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__pycache__
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*_code.gen.*
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/NeuroML2/GranuleCell/*.dat
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/NeuroML2/GranuleCell/*.mod
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/NeuroML2/GranuleCell/*.hoc
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*_eden.py
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*_netpyne.py
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/NeuroML2/GranuleCell/simulator.props

‎NeuroML2/GranuleCell/.test.jnml.omt‎

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# Script for running automated tests on OSB models, see https://github.com/OpenSourceBrain/osb-model-validation
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target: LEMS_GranuleCell_LowDt.xml
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engine: jNeuroML
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mep: .test.mep
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experiments:
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Current clamp:
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observables:
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spike times:
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file:
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path: Gran_0.dat
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columns: [0,1]
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scaling: [1000, 1000]
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spike detection:
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method: threshold
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threshold: 0
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tolerance: 0.005045 # relatively large tolerance due to inaccuracy of jLEMS's simple numerical integration methods
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Ca concentration:
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observables:
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spike times:
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file:
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path: Gran_0.Gran_CaPool_98_CONC_ca.dat
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columns: [0,1]
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scaling: [1000, 1]
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spike detection:
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method: threshold
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threshold: 0.0016
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tolerance: 0.005006

‎NeuroML2/GranuleCell/.test.jnmleden.omt‎

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# Script for running automated tests on OSB models, see https://github.com/OpenSourceBrain/osb-model-validation
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target: LEMS_GranuleCell_LowDt.xml
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engine: jNeuroML_EDEN
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mep: .test.mep
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experiments:
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Current clamp:
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observables:
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spike times:
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file:
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path: Gran_0.dat
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columns: [0,1]
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scaling: [1000, 1000]
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spike detection:
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method: threshold
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threshold: 0
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tolerance: 0.0009466481426621017
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Ca concentration:
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observables:
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spike times:
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file:
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path: Gran_0.Gran_CaPool_98_CONC_ca.dat
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columns: [0,1]
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scaling: [1000, 1]
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spike detection:
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method: threshold
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threshold: 0.0016
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tolerance: 0.0009426209285668374

‎NeuroML2/GranuleCell/.test.jnmlnetpyne.omt‎

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# Script for running automated tests on OSB models, see https://github.com/OpenSourceBrain/osb-model-validation
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target: LEMS_GranuleCell_LowDt.xml
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engine: jNeuroML_NetPyNE
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mep: .test.mep
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experiments:
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Current clamp:
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observables:
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spike times:
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file:
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path: Gran_0.dat
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columns: [0,1]
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scaling: [1000, 1000]
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spike detection:
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method: threshold
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threshold: 0
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tolerance: 0.0000
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Ca concentration:
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observables:
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spike times:
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file:
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path: Gran_0.Gran_CaPool_98_CONC_ca.dat
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columns: [0,1]
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scaling: [1000, 1]
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spike detection:
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method: threshold
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threshold: 0.0016
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tolerance: 0.0000

‎NeuroML2/GranuleCell/.test.jnmlnrn.omt‎

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# Script for running automated tests on OSB models, see https://github.com/OpenSourceBrain/osb-model-validation
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target: LEMS_GranuleCell_LowDt.xml
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engine: jNeuroML_NEURON
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mep: .test.mep
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experiments:
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Current clamp:
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observables:
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spike times:
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file:
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path: Gran_0.dat
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columns: [0,1]
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scaling: [1000, 1000]
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spike detection:
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method: threshold
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threshold: 0
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tolerance: 0.0000
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Ca concentration:
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observables:
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spike times:
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file:
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path: Gran_0.Gran_CaPool_98_CONC_ca.dat
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columns: [0,1]
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scaling: [1000, 1]
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spike detection:
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method: threshold
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threshold: 0.0016
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tolerance: 0.0000

‎NeuroML2/GranuleCell/.test.mep‎

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system: Testing a single compartment cell
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experiments:
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Current clamp:
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expected:
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spike times: [108.261, 135.871, 161.896, 187.363, 212.561, 237.626, 262.629, 287.602, 312.565, 337.526, 362.49, 387.457, 412.43, 437.406, 462.388, 487.373, 512.362, 537.354, 562.349, 587.346]
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Ca concentration:
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expected:
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spike times: [108.602, 136.257, 162.294, 187.764, 212.961, 238.024, 263.023, 287.994, 312.955, 337.913, 362.875, 387.841, 412.812, 437.788, 462.768, 487.752, 512.741, 537.732, 562.726, 587.723]

‎NeuroML2/GranuleCell/.test.validate.omt‎

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# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
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# Still in development, subject to change without notice!!
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# This test will validate all of the NeuroML 2 files in the current directory using: jnml -validate *.nml
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target: "*.nml"
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engine: jNeuroML_validate

‎NeuroML2/GranuleCell/GranPassiveCond.channel.nml‎

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<?xml version="1.0" encoding="ISO-8859-1"?>
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<neuroml xmlns="http://www.neuroml.org/schema/neuroml2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd" id="GranPassiveCond">
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<notes>ChannelML file containing a single Channel description</notes>
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<ionChannel id="GranPassiveCond" conductance="10pS" type="ionChannelPassive">
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<notes>Simple leak conductance for Granule cell</notes>
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<annotation>
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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
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<rdf:Description rdf:about="GranPassiveCond">
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<bqmodel:isDescribedBy xmlns:bqmodel="http://biomodels.net/model-qualifiers/">
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<rdf:Bag>
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<rdf:li>Maex, R and De Schutter, E.
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Synchronization of Golgi and Granule Cell Firing in a Detailed Network Model of the
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cerebellar Granule Cell Layer. J Neurophysiol, Nov 1998; 80: 2521 - 2537</rdf:li>
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<rdf:li rdf:resource="http://www.ncbi.nlm.nih.gov/pubmed/9819260"/>
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</rdf:Bag>
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</bqmodel:isDescribedBy>
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</rdf:Description>
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</rdf:RDF>
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</annotation>
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</ionChannel>
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</neuroml>

‎NeuroML2/GranuleCell/Gran_CaHVA_98.channel.nml‎

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<?xml version="1.0" encoding="ISO-8859-1"?>
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<neuroml xmlns="http://www.neuroml.org/schema/neuroml2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd" id="Gran_CaHVA_98">
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<notes>A channel from Maex, R and De Schutter, E. Synchronization of Golgi and Granule Cell Firing in a
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Detailed Network Model of the Cerebellar Granule Cell Layer</notes>
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<ionChannel id="Gran_CaHVA_98" conductance="10pS" type="ionChannelHH" species="ca">
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<notes>A High Voltage Activated Ca2+ channel</notes>
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<annotation>
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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
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<rdf:Description rdf:about="Gran_CaHVA_98">
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<bqmodel:isDescribedBy xmlns:bqmodel="http://biomodels.net/model-qualifiers/">
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<rdf:Bag>
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<rdf:li>Maex, R and De Schutter, E.
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Synchronization of Golgi and Granule Cell Firing in a Detailed Network Model of the
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cerebellar Granule Cell Layer. J Neurophysiol, Nov 1998; 80: 2521 - 2537</rdf:li>
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<rdf:li rdf:resource="http://www.ncbi.nlm.nih.gov/pubmed/9819260"/>
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</rdf:Bag>
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</bqmodel:isDescribedBy>
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<bqbiol:isVersionOf xmlns:bqbiol="http://biomodels.net/biology-qualifiers/">
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<rdf:Bag>
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<rdf:li>Calcium channels</rdf:li>
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<rdf:li rdf:resource="http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table1"/>
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</rdf:Bag>
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</bqbiol:isVersionOf>
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</rdf:Description>
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</rdf:RDF>
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</annotation>
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<gate id="m" type="gateHHrates" instances="2">
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<q10Settings type="q10ExpTemp" q10Factor="3" experimentalTemp="17.350264793 degC"/>
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<!--Note: offset from ChannelML file incorporated into the midpoint of rates!!-->
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<forwardRate type="HHSigmoidRate" rate="1600per_s" scale="0.01388888889V" midpoint="0.015V"/>
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<reverseRate type="HHExpLinearRate" rate="100per_s" scale="-0.005V" midpoint="0.0011000000000000003V"/>
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</gate>
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<gate id="h" type="gateHHrates" instances="1">
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<q10Settings type="q10ExpTemp" q10Factor="3" experimentalTemp="17.350264793 degC"/>
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<!--Note: offset from ChannelML file incorporated into the midpoint of rates!!-->
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<forwardRate type="Gran_CaHVA_98_h_alpha_rate"/>
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<reverseRate type="Gran_CaHVA_98_h_beta_rate"/>
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</gate>
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</ionChannel>
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<ComponentType name="Gran_CaHVA_98_h_alpha_rate" extends="baseVoltageDepRate">
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<Constant name="TIME_SCALE" dimension="time" value="1 s"/>
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<Constant name="VOLT_SCALE" dimension="voltage" value="1 V"/>
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<Constant name="offset" dimension="voltage" value="0.010V"/>
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<Dynamics>
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<DerivedVariable name="V" dimension="none" value="(v - offset) / VOLT_SCALE"/>
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<ConditionalDerivedVariable name="r" exposure="r" dimension="per_time">
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<Case condition="V .lt. ( -0.060 )" value="( 5.0 ) / TIME_SCALE"/>
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<Case value="( 5 * (exp (-50 * (V - (-0.060))))) / TIME_SCALE"/>
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</ConditionalDerivedVariable>
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</Dynamics>
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</ComponentType>
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<ComponentType name="Gran_CaHVA_98_h_beta_rate" extends="baseVoltageDepRate">
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<Constant name="TIME_SCALE" dimension="time" value="1 s"/>
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<Constant name="VOLT_SCALE" dimension="voltage" value="1 V"/>
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<Constant name="offset" dimension="voltage" value="0.010V"/>
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<Dynamics>
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<DerivedVariable name="V" dimension="none" value="(v - offset) / VOLT_SCALE"/>
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<ConditionalDerivedVariable name="r" exposure="r" dimension="per_time">
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<Case condition="V .lt. ( -0.060 )" value="( 0 ) / TIME_SCALE"/>
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<Case value="( 5 - (5 * (exp (-50 * (V - (-0.060)))))) / TIME_SCALE"/>
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</ConditionalDerivedVariable>
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</Dynamics>
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</ComponentType>
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</neuroml>

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