├── .github └── workflows │ ├── ci.yml │ └── non_omv.yml ├── .gitignore ├── .readthedocs.yaml ├── .travis.yml ├── NeuroML2 └── prototypes │ ├── AllenInstituteCellTypesDB_HH │ ├── HH_476686112.cell.nml │ ├── HH_477127614.cell.nml │ ├── IMchan.channel.nml │ ├── Kd.channel.nml │ ├── Leak.channel.nml │ ├── Na.channel.nml │ └── README.md │ ├── BlueBrainProject_NMC │ ├── Ca.channel.nml │ ├── CaDynamics_E2_NML2.nml │ ├── Ca_HVA.channel.nml │ ├── Ca_LVAst.channel.nml │ ├── Ih.channel.nml │ ├── Im.channel.nml │ ├── K_Pst.channel.nml │ ├── K_Tst.channel.nml │ ├── KdShu2007.channel.nml │ ├── NaTa_t.channel.nml │ ├── NaTs2_t.channel.nml │ ├── Nap_Et2.channel.nml │ ├── README.md │ ├── SK_E2.channel.nml │ ├── SKv3_1.channel.nml │ ├── StochKv_deterministic.channel.nml │ ├── cADpyr229_L23_PC_5ecbf9b163_0_0.cell.nml │ └── pas.channel.nml │ ├── L23Pyr_SmithEtAl2013 │ ├── .test.validate.omt │ ├── AMPA.synapse.nml │ ├── AMPA_NMDA.synapse.nml │ ├── GABA.synapse.nml │ ├── L23_NoHotSpot.cell.nml │ ├── NMDA.synapse.nml │ ├── ca.channel.nml │ ├── it.channel.nml │ ├── kca.channel.nml │ ├── km.channel.nml │ ├── kv.channel.nml │ ├── na.channel.nml │ └── pas.channel.nml │ ├── Thalamocortical │ ├── L23PyrFRB.cell.nml │ ├── L23PyrRS.cell.nml │ ├── SupBasket.cell.nml │ ├── ar.channel.nml │ ├── ar__m00.channel.nml │ ├── ar__m00_25.channel.nml │ ├── cad__beta0_01__phi26000.nml │ ├── cad__beta0_02__phi260000.nml │ ├── cad__beta0_05__phi52000.nml │ ├── cad__beta0_05__phi520000.nml │ ├── cal.channel.nml │ ├── cat.channel.nml │ ├── cat_a.channel.nml │ ├── k2.channel.nml │ ├── ka.channel.nml │ ├── ka_ib.channel.nml │ ├── kahp.channel.nml │ ├── kahp_deeppyr.channel.nml │ ├── kahp_slower.channel.nml │ ├── kc.channel.nml │ ├── kc_fast.channel.nml │ ├── kdr.channel.nml │ ├── kdr_fs.channel.nml │ ├── km.channel.nml │ ├── naf.channel.nml │ ├── naf2.channel.nml │ ├── naf2__a0__b0__c0__d0__fastNa_shift0.channel.nml │ ├── naf2__a0__b0__c0__d0__fastNa_shiftmin2_5.channel.nml │ ├── naf__a0__b0__c0__d0__fastNa_shiftmin3_5.channel.nml │ ├── naf_tcr.channel.nml │ ├── naf_tcr__shift_hnafmin7__shift_mnaf_initmin3__shift_mnaf_runmin2_5.channel.nml │ ├── nap.channel.nml │ ├── napf.channel.nml │ ├── napf__a0__b0__c0__d0__fastNa_shift0.channel.nml │ ├── napf_spinstell.channel.nml │ ├── napf_spinstell__a0__b0__c0__d0__fastNa_shiftmin2_5.channel.nml │ ├── napf_tcr.channel.nml │ ├── napf_tcr__a0__b0__c0__d0__fastNa_shift7.channel.nml │ ├── netConnList │ └── pas.channel.nml │ ├── acnet2 │ ├── AMPA_syn.synapse.nml │ ├── AMPA_syn_inh.synapse.nml │ ├── Ca_conc.nml │ ├── Ca_pyr.channel.nml │ ├── GABA_syn.synapse.nml │ ├── GABA_syn_inh.synapse.nml │ ├── Kahp_pyr.channel.nml │ ├── Kdr_bask.channel.nml │ ├── Kdr_pyr.channel.nml │ ├── LeakConductance_bask.channel.nml │ ├── LeakConductance_pyr.channel.nml │ ├── Na_bask.channel.nml │ ├── Na_pyr.channel.nml │ ├── bask.cell.nml │ ├── pyr_4_sym.cell.nml │ └── pyr_4_sym_soma.cell.nml │ ├── iaf │ ├── iaf.cell.nml │ └── iafRef.cell.nml │ └── izhikevich │ ├── Izh_471141261.cell.nml │ └── RS.cell.nml ├── README.md ├── doc ├── Makefile ├── conf.py ├── how_to_contribute.txt ├── index.txt ├── install.txt ├── introduction.txt ├── logo.png ├── modules.txt ├── opencortex.build.txt ├── opencortex.core.txt ├── opencortex.txt └── opencortex.utils.txt ├── examples ├── ACNet.net.nml ├── ACNet.py ├── ACNet │ ├── Ca_conc.nml │ ├── Ca_pyr.channel.nml │ ├── Kahp_pyr.channel.nml │ ├── Kdr_bask.channel.nml │ ├── Kdr_pyr.channel.nml │ ├── LeakConductance_bask.channel.nml │ ├── LeakConductance_pyr.channel.nml │ ├── Na_bask.channel.nml │ ├── Na_pyr.channel.nml │ ├── bask.cell.nml │ └── pyr_4_sym.cell.nml ├── Balanced.net.nml ├── Balanced.py ├── Balanced │ ├── CaDynamics_E2_NML2.nml │ ├── Ca_HVA.channel.nml │ ├── Ca_LVAst.channel.nml │ ├── HH_476686112.cell.nml │ ├── HH_477127614.cell.nml │ ├── IMchan.channel.nml │ ├── Ih.channel.nml │ ├── Im.channel.nml │ ├── K_Pst.channel.nml │ ├── K_Tst.channel.nml │ ├── Kd.channel.nml │ ├── Leak.channel.nml │ ├── Na.channel.nml │ ├── NaTa_t.channel.nml │ ├── NaTs2_t.channel.nml │ ├── Nap_Et2.channel.nml │ ├── SK_E2.channel.nml │ ├── SKv3_1.channel.nml │ ├── cADpyr229_L23_PC_5ecbf9b163_0_0.cell.nml │ └── pas.channel.nml ├── Balanced_240cells_36926conns.net.nml ├── Balanced_24cells_336conns.net.nml ├── Balanced_24cells_336conns │ ├── HH_476686112.cell.nml │ ├── HH_477127614.cell.nml │ ├── IMchan.channel.nml │ ├── Kd.channel.nml │ ├── Leak.channel.nml │ └── Na.channel.nml ├── Balanced_610cells_0conns.net.nml ├── Balanced_610cells_213550conns.net.nml ├── Complex.net.nml ├── Complex.py ├── Complex │ ├── Ca_conc.nml │ ├── Ca_pyr.channel.nml │ ├── Kahp_pyr.channel.nml │ ├── Kdr_pyr.channel.nml │ ├── LeakConductance_pyr.channel.nml │ ├── Na_pyr.channel.nml │ ├── RS.cell.nml │ ├── iaf.cell.nml │ ├── iafRef.cell.nml │ ├── pyr_4_sym.cell.nml │ └── pyr_4_sym_soma.cell.nml ├── Deterministic.net.nml ├── Deterministic.py ├── Deterministic │ ├── RS.cell.nml │ └── iaf.cell.nml ├── GapJunctions.net.nml ├── GapJunctions.py ├── GapJunctions │ ├── Ca_conc.nml │ ├── Ca_pyr.channel.nml │ ├── Kahp_pyr.channel.nml │ ├── Kdr_pyr.channel.nml │ ├── LeakConductance_pyr.channel.nml │ ├── Na_pyr.channel.nml │ └── pyr_4_sym.cell.nml ├── HDF5 │ ├── ACNet.net.nml.h5 │ ├── ACNet │ │ ├── Ca_conc.nml │ │ ├── Ca_pyr.channel.nml │ │ ├── Kahp_pyr.channel.nml │ │ ├── Kdr_bask.channel.nml │ │ ├── Kdr_pyr.channel.nml │ │ ├── LeakConductance_bask.channel.nml │ │ ├── LeakConductance_pyr.channel.nml │ │ ├── Na_bask.channel.nml │ │ ├── Na_pyr.channel.nml │ │ ├── bask.cell.nml │ │ └── pyr_4_sym.cell.nml │ ├── IClamps.net.nml.h5 │ ├── IClamps │ │ ├── Ca_conc.nml │ │ ├── Ca_pyr.channel.nml │ │ ├── Kahp_pyr.channel.nml │ │ ├── Kdr_pyr.channel.nml │ │ ├── LeakConductance_pyr.channel.nml │ │ ├── Na_pyr.channel.nml │ │ ├── RS.cell.nml │ │ └── pyr_4_sym_soma.cell.nml │ ├── LEMS_ACNet.xml │ ├── LEMS_IClamps.xml │ ├── LEMS_SpikingNet.xml │ ├── SpikingNet.net.nml.h5 │ ├── SpikingNet │ │ └── RS.cell.nml │ ├── Weights.net.nml.h5 │ └── Weights │ │ ├── HH_477127614.cell.nml │ │ ├── IMchan.channel.nml │ │ ├── Kd.channel.nml │ │ ├── Leak.channel.nml │ │ └── Na.channel.nml ├── IClamps.net.nml ├── IClamps.py ├── IClamps │ ├── Ca_conc.nml │ ├── Ca_pyr.channel.nml │ ├── Kahp_pyr.channel.nml │ ├── Kdr_pyr.channel.nml │ ├── LeakConductance_pyr.channel.nml │ ├── Na_pyr.channel.nml │ ├── RS.cell.nml │ └── pyr_4_sym_soma.cell.nml ├── L23TraubDemo.net.nml ├── L23TraubDemo.py ├── L23TraubDemo │ ├── L23PyrRS.cell.nml │ ├── SupBasket.cell.nml │ ├── ar__m00_25.channel.nml │ ├── cad__beta0_01__phi26000.nml │ ├── cad__beta0_02__phi260000.nml │ ├── cad__beta0_05__phi52000.nml │ ├── cad__beta0_05__phi520000.nml │ ├── cal.channel.nml │ ├── cat.channel.nml │ ├── k2.channel.nml │ ├── ka.channel.nml │ ├── kahp.channel.nml │ ├── kahp_slower.channel.nml │ ├── kc.channel.nml │ ├── kc_fast.channel.nml │ ├── kdr.channel.nml │ ├── kdr_fs.channel.nml │ ├── km.channel.nml │ ├── naf2__a0__b0__c0__d0__fastNa_shiftmin2_5.channel.nml │ ├── naf__a0__b0__c0__d0__fastNa_shiftmin3_5.channel.nml │ ├── nap.channel.nml │ └── pas.channel.nml ├── LEMS_ACNet.xml ├── LEMS_Balanced.xml ├── LEMS_Balanced_24cells_336conns.xml ├── LEMS_Balanced_610cells_0conns.xml ├── LEMS_Complex.xml ├── LEMS_Deterministic.xml ├── LEMS_GapJunctions.xml ├── LEMS_IClamps.xml ├── LEMS_L23TraubDemo.xml ├── LEMS_Recording.xml ├── LEMS_SimpleNet.xml ├── LEMS_SpikingNet.xml ├── LEMS_VClamp.xml ├── LEMS_Weights.xml ├── Multiscale.py ├── Recording.net.nml ├── Recording.py ├── Recording │ ├── Ca_conc.nml │ ├── Ca_pyr.channel.nml │ ├── Kahp_pyr.channel.nml │ ├── Kdr_pyr.channel.nml │ ├── LeakConductance_pyr.channel.nml │ ├── Na_pyr.channel.nml │ └── pyr_4_sym.cell.nml ├── SimpleNet.net.nml ├── SimpleNet.py ├── SimpleNet │ └── RS.cell.nml ├── SpikingNet.net.nml ├── SpikingNet.py ├── SpikingNet │ └── RS.cell.nml ├── VClamp.net.nml ├── VClamp.py ├── VClamp │ ├── L23PyrRS.cell.nml │ ├── ar__m00_25.channel.nml │ ├── cad__beta0_01__phi26000.nml │ ├── cad__beta0_05__phi52000.nml │ ├── cal.channel.nml │ ├── cat.channel.nml │ ├── k2.channel.nml │ ├── ka.channel.nml │ ├── kahp.channel.nml │ ├── kc.channel.nml │ ├── kdr.channel.nml │ ├── km.channel.nml │ ├── naf__a0__b0__c0__d0__fastNa_shiftmin3_5.channel.nml │ ├── nap.channel.nml │ └── pas.channel.nml ├── Weights.net.nml ├── Weights.py ├── Weights │ ├── HH_477127614.cell.nml │ ├── IMchan.channel.nml │ ├── Kd.channel.nml │ ├── Leak.channel.nml │ └── Na.channel.nml ├── netpyneLoad.py ├── neuromllite │ ├── LEMS_SimSimpleNet.xml │ ├── README.md │ ├── SimSimpleNet.json │ ├── SimpleNet.json │ ├── SimpleNet.net.nml │ └── SimpleNet.py ├── redo.sh ├── regenerateAll.sh ├── test_scaling.py └── tests │ ├── .test.acnet.h5.jnmlnetpyne.omt │ ├── .test.acnet.h5.jnmlnrn.omt │ ├── .test.acnet.jnmlnetpyne.omt │ ├── .test.acnet.jnmlnetpyne4.omt_ │ ├── .test.acnet.jnmlnrn.omt │ ├── .test.acnet.mep │ ├── .test.balanced.jnmlnetpyne.omt │ ├── .test.balanced.jnmlnrn.omt │ ├── .test.balanced.mep │ ├── .test.complex.jnmlnetpyne.omt │ ├── .test.complex.jnmlnrn.omt │ ├── .test.deterministic.jnml.omt │ ├── .test.deterministic.jnmlnetpyne.omt │ ├── .test.deterministic.jnmlnrn.omt │ ├── .test.deterministic.mep │ ├── .test.iclamps.h5.jnmlnetpyne.omt │ ├── .test.iclamps.h5.jnmlnetpyne2.omt_ │ ├── .test.iclamps.h5.jnmlnrn.omt │ ├── .test.iclamps.jnml.omt │ ├── .test.iclamps.jnmlnetpyne.omt │ ├── .test.iclamps.jnmlnrn.omt │ ├── .test.iclamps.jnmlpynnnrn.omt │ ├── .test.iclamps.mep │ ├── .test.l23traub.mep │ ├── .test.l23traubdemo.jnmlnetpyne.omt │ ├── .test.l23traubdemo.jnmlnetpyne2.omt_ │ ├── .test.l23traubdemo.jnmlnrn.omt │ ├── .test.recording.jnmlnrn.omt │ ├── .test.simple.jnml.mep │ ├── .test.simple.jnml.omt │ ├── .test.simple.jnmlnetpyne.omt │ ├── .test.simple.jnmlnetpyne2.omt_ │ ├── .test.simple.jnmlnrn.mep │ ├── .test.simple.jnmlnrn.omt │ ├── .test.spiking.h5.jnmlnetpyne.omt │ ├── .test.spiking.h5.jnmlnetpyne2.omt_ │ ├── .test.spiking.h5.jnmlnrn.omt │ ├── .test.spiking.jnml.omt │ ├── .test.spiking.jnmlnetpyne.omt │ ├── .test.spiking.jnmlnetpyne2.omt_ │ ├── .test.spiking.jnmlnetpyne4.omt_ │ ├── .test.spiking.jnmlnrn.omt │ ├── .test.spiking.jnmlpynnnrn.omt │ ├── .test.spiking.nrn.mep │ ├── .test.validate.omt │ └── .test.vclamp.jnmlnrn.omt ├── notebooks └── TestIClamp.ipynb ├── opencortex ├── __init__.py ├── build │ └── __init__.py ├── core │ └── __init__.py ├── test │ ├── ConnListTest │ ├── README.md │ ├── Test.cell.nml │ ├── Test2.cell.nml │ ├── test_build_methods.py │ └── test_utils_methods.py └── utils │ ├── __init__.py │ └── color.py └── setup.py /.github/workflows/ci.yml: -------------------------------------------------------------------------------- 1 | name: Continuous builds - OMV tests 2 | 3 | on: 4 | push: 5 | branches: [ master, development, experimental ] 6 | pull_request: 7 | branches: [ master, development, experimental ] 8 | 9 | jobs: 10 | build: 11 | 12 | runs-on: ubuntu-latest 13 | strategy: 14 | fail-fast: false 15 | matrix: 16 | python-version: [ 3.8, 3.9, "3.10" ] 17 | engine: [ jNeuroML, jNeuroML_NEURON, jNeuroML_NetPyNE, jNeuroML_validate, jNeuroML_PyNN_NEURON ] 18 | 19 | steps: 20 | - uses: actions/checkout@v3 21 | - name: Set up Python ${{ matrix.python-version }} 22 | uses: actions/setup-python@v3 23 | with: 24 | python-version: ${{ matrix.python-version }} 25 | - name: Install OMV 26 | run: | 27 | pip install git+https://github.com/OpenSourceBrain/osb-model-validation 28 | pip install scipy sympy matplotlib cython pandas tables 29 | 30 | pip install 'numpy<=1.23.0' # see https://github.com/OpenSourceBrain/osb-model-validation/issues/91 31 | 32 | - name: Run OMV tests on engine ${{ matrix.engine }} 33 | run: | 34 | omv all -V --engine=${{ matrix.engine }} 35 | - name: OMV final version info 36 | run: | 37 | omv list -V # list installed engines 38 | -------------------------------------------------------------------------------- /.github/workflows/non_omv.yml: -------------------------------------------------------------------------------- 1 | name: Non OMV tests 2 | 3 | on: 4 | push: 5 | branches: [ master, development, experimental ] 6 | pull_request: 7 | branches: [ master, development, experimental ] 8 | 9 | jobs: 10 | build: 11 | 12 | runs-on: ubuntu-latest 13 | strategy: 14 | matrix: 15 | python-version: [ 3.8, 3.9 ] 16 | 17 | steps: 18 | - uses: actions/checkout@v3 19 | - name: Set up Python ${{ matrix.python-version }} 20 | uses: actions/setup-python@v3 21 | with: 22 | python-version: ${{ matrix.python-version }} 23 | - name: Install OpenCortex 24 | run: | 25 | pip install . 26 | - name: Run some examples 27 | run: | 28 | echo "Running non OMV tests..." 29 | cd examples 30 | ./regenerateAll.sh 31 | ls -alt 32 | cd ../opencortex/test 33 | pip install pytest 34 | pytest -vs 35 | pip freeze 36 | -------------------------------------------------------------------------------- /.readthedocs.yaml: -------------------------------------------------------------------------------- 1 | # .readthedocs.yaml 2 | # Read the Docs configuration file 3 | # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details 4 | 5 | # Required 6 | version: 2 7 | 8 | # Set the version of Python and other tools you might need 9 | build: 10 | os: ubuntu-22.04 11 | tools: 12 | python: "3.11" 13 | 14 | # Build documentation in the docs/ directory with Sphinx 15 | sphinx: 16 | configuration: doc/conf.py 17 | 18 | # We recommend specifying your dependencies to enable reproducible builds: 19 | # https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html 20 | # python: 21 | # install: 22 | # - requirements: docs/requirements.txt 23 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | # Framework for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | dist: xenial 4 | sudo: false 5 | 6 | addons: 7 | apt: 8 | packages: 9 | #- python-matplotlib 10 | #- python-numpy 11 | #- python-scipy 12 | - python-tk 13 | - python-lxml 14 | - python-tornado 15 | - python-tables 16 | 17 | language: python 18 | python: 19 | - 2.7 20 | #- 3.6 21 | 22 | env: 23 | - OMV_ENGINE=jNeuroML 24 | - OMV_ENGINE=jNeuroML_NEURON 25 | - OMV_ENGINE=jNeuroML_NetPyNE 26 | - OMV_ENGINE=jNeuroML_PyNN_NEURON 27 | - OMV_ENGINE=jNeuroML_validate 28 | - OMV_ENGINE=NON_OMV_TESTS 29 | 30 | install: 31 | - git clone https://github.com/OpenSourceBrain/osb-model-validation 32 | - cd osb-model-validation 33 | - python setup.py install 34 | - cd .. 35 | 36 | - pip install matplotlib>=2.2.5 # This is more because Matplotlib v2.x is required... 37 | - pip install future pandas matplotlib-scalebar bokeh scipy # For NetPyNE... 38 | - pip install tables # Ditto 39 | 40 | - pip install . 41 | 42 | script: 43 | - omv all -V; export OMV_SUCCESS=$?; echo $OMV_SUCCESS 44 | - echo "Finished all OMV tests" 45 | - omv list-engines -V 46 | - if [[ ${OMV_ENGINE} == "NON_OMV_TESTS" ]]; then echo "Continuing with tests not under OMV..."; else exit $OMV_SUCCESS; fi 47 | - echo "Running non OMV tests..." 48 | - cd examples 49 | - ./regenerateAll.sh 50 | - ls -alt 51 | - cd ../opencortex/test 52 | - nosetests -vs 53 | 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/AllenInstituteCellTypesDB_HH/Leak.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file containing a single Channel description 5 | 6 | 7 | 8 | ChannelML file containing a single Channel description 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/AllenInstituteCellTypesDB_HH/README.md: -------------------------------------------------------------------------------- 1 | 2 | #### Allen CellTypes Database 3 | 4 | Data and models from the [Allen CellTypes Database](http://celltypes.brain-map.org/) has been used to 5 | construct NeuroML based models. See https://github.com/OpenSourceBrain/AllenInstituteNeuroML/tree/master/CellTypesDatabase. 6 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/Ca.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | NeuroML file containing a single Channel description 9 | 10 | Note: was called Ca_HVA in Hay et al 2011: http://www.opensourcebrain.org/projects/l5bpyrcellhayetal2011 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 21 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | Calcium channels 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/Ca_HVA.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | High voltage activated Ca2+ current. 9 | 10 | Comment from original mod file: 11 | Reuveni, Friedman, Amitai, and Gutnick, J.Neurosci. 1993 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 20 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | Calcium channels 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/Ih.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Non-specific cation current 9 | 10 | Comment from original mod file: 11 | Reference : : Kole,Hallermann,and Stuart, J. Neurosci. 2006 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 20 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/Im.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Muscarinic K+ current 9 | 10 | Comment from original mod file: 11 | :Reference : : Adams et al. 1982 - M-currents and other potassium currents in bullfrog sympathetic neurones 12 | :Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 21 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | K channels 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/KdShu2007.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 6 | 7 | NeuroML file containing a single Channel description 8 | 9 | 10 | 11 | K-D current for prefrontal cortical neuron - Yuguo Yu 2007 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | K channels 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 31 | 32 | 33 | 34 | 35 | 36 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/NaTa_t.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Fast inactivating Na+ current 9 | 10 | Comment from original mod file: 11 | :Reference :Colbert and Pan 2002 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 20 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | Na channels 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/NaTs2_t.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Fast inactivating Na+ current. Comment from mod file (NaTs2_t.mod): took the NaTa and shifted both activation/inactivation by 6 mv 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 17 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | Na channels 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/README.md: -------------------------------------------------------------------------------- 1 | #### BBP Neocortical Microcircuit to NeuroML 2 2 | 3 | This directory contains code for converting cell models from the [Neocortical Microcircuit Collaboration Portal](https://bbp.epfl.ch/nmc-portal/microcircuit) 4 | to NeuroML 2. 5 | 6 | 7 | See [here](https://github.com/OpenSourceBrain/BlueBrainProjectShowcase/blob/master/NMC/NeuroML2/README.md) for more. 8 | 9 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/StochKv_deterministic.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML2 file containing a single Channel description 5 | 6 | 7 | 8 | Deterministic version of StochKv channel. See https://github.com/OpenSourceBrain/BlueBrainProjectShowcase/tree/master/NMC/NEURON/test 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | K channels 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/BlueBrainProject_NMC/pas.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Simple example of a leak/passive conductance. Note: for GENESIS cells with a single leak conductance, 9 | it is better to use the Rm and Em variables for a passive current. 10 | 11 | 12 | 13 | 14 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/L23Pyr_SmithEtAl2013/.test.validate.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | # This test will validate all of the NeuroML 2 files in the current directory using: jnml -validate *.nml 4 | target: "*.nml" 5 | engine: jNeuroML_validate 6 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/L23Pyr_SmithEtAl2013/AMPA.synapse.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML 2 file describing a single synaptic mechanism 5 | 6 | 7 | AMPA synapse 8 | 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/L23Pyr_SmithEtAl2013/AMPA_NMDA.synapse.nml: -------------------------------------------------------------------------------- 1 | 2 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | A single "synapse" which contains both AMPA and NMDA. Hopefully the need for extra synapse1Path/synapse2Path attributes can be removed in later versions. 13 | 14 | 15 | 16 | 17 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/L23Pyr_SmithEtAl2013/GABA.synapse.nml: -------------------------------------------------------------------------------- 1 | 2 | 6 | 7 | NeuroML 2 file describing a single synaptic mechanism 8 | 9 | 10 | GABA synapse 11 | 12 | 13 | 14 | 15 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/L23Pyr_SmithEtAl2013/NMDA.synapse.nml: -------------------------------------------------------------------------------- 1 | 2 | 6 | 7 | NeuroML 2 file describing a single synaptic mechanism 8 | 9 | 10 | 12 | NMDA synapse 13 | 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/L23Pyr_SmithEtAl2013/kca.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 6 | 7 | WARNING: in the orginial mod file the conductance was multiplied by e-4, remember to scale your Gmax 8 | 9 | 10 | 11 | Comment from original mod file: Calcium-dependent potassium channel, 12 | Based on Pennefather (1990) -- sympathetic ganglion cells, 13 | taken from Reuveni et al (1993) -- neocortical cells 14 | Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/L23Pyr_SmithEtAl2013/pas.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Simple example of a leak/passive conductance. 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/Thalamocortical/cad__beta0_01__phi26000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/Thalamocortical/cad__beta0_02__phi260000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/Thalamocortical/cad__beta0_05__phi52000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/Thalamocortical/cad__beta0_05__phi520000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/Thalamocortical/pas.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file containing a single Channel description 5 | 6 | 7 | 8 | Simple passive leak conductance 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/AMPA_syn.synapse.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing a single synaptic mechanism 5 | 6 | 7 | 8 | Simple example of a synaptic mechanism, which consists of a postsynaptic conductance which changes as a double exponential function of time. Mappings exist for NEURON and GENESIS. 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/AMPA_syn_inh.synapse.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing a single synaptic mechanism 5 | 6 | 7 | 8 | 9 | Simple example of a synaptic mechanism, which consists of a postsynaptic conductance which changes as a double exponential function of time. Mappings exist for NEURON and GENESIS. 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/Ca_conc.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing intracellular Calcium dynamics, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/GABA_syn.synapse.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing a single synaptic mechanism 5 | 6 | 7 | 8 | Simple example of a synaptic mechanism, which consists of a postsynaptic conductance which changes as a double exponential function of time. Mappings exist for NEURON and GENESIS. 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/GABA_syn_inh.synapse.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing a single synaptic mechanism 5 | 6 | 7 | 8 | NOTE: zero conductance as per ACnet2-main.g 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/Kdr_bask.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the book Neuronal Networks of the Hippocampus, Traub and Miles 1991 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the book Neuronal Networks of the Hippocampus, Traub and Miles 1991 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., & Miles, R. (1991). Neuronal Networks of the Hippocampus. Cambridge University Press. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/Kdr_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/LeakConductance_bask.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for basket cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/LeakConductance_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for pyramidal cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/acnet2/Na_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | Na channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/iaf/iaf.cell.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 7 | 8 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/iaf/iafRef.cell.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 7 | 8 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /NeuroML2/prototypes/izhikevich/RS.cell.nml: -------------------------------------------------------------------------------- 1 | 6 | 7 | 10 | 11 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## OpenCortex 2 | 3 | A framework for building cortical network models on OSB in NeuroML format. 4 | 5 | In development, subject to change without notice... Contact P Gleeson for more details. 6 | 7 | **Note: much of this functionality has been replaced by: https://github.com/NeuroML/NeuroMLlite** 8 | 9 | [![Continuous builds - OMV tests](https://github.com/OpenSourceBrain/OpenCortex/actions/workflows/ci.yml/badge.svg)](https://github.com/OpenSourceBrain/OpenCortex/actions/workflows/ci.yml) 10 | [![Non OMV tests](https://github.com/OpenSourceBrain/OpenCortex/actions/workflows/non_omv.yml/badge.svg)](https://github.com/OpenSourceBrain/OpenCortex/actions/workflows/non_omv.yml) 11 | -------------------------------------------------------------------------------- /doc/how_to_contribute.txt: -------------------------------------------------------------------------------- 1 | How to contribute 2 | ================= 3 | 4 | Contact: p dot gleeson at gmail dot com 5 | -------------------------------------------------------------------------------- /doc/index.txt: -------------------------------------------------------------------------------- 1 | ========================= 2 | OpenCortex: documentation 3 | ========================= 4 | 5 | .. toctree:: 6 | :maxdepth: 2 7 | 8 | introduction 9 | install 10 | 11 | ======================= 12 | Developer documentation 13 | ======================= 14 | 15 | .. toctree:: 16 | :maxdepth: 2 17 | 18 | how_to_contribute 19 | modules 20 | 21 | 22 | .. .. Source documentation 23 | .. ==================== 24 | .. 25 | .. .. toctree:: 26 | .. :maxdepth: 3 27 | .. 28 | .. modules 29 | 30 | .. Indices and tables 31 | .. ================== 32 | 33 | .. * :ref:`genindex` 34 | .. * :ref:`modindex` 35 | .. * :ref:`search` 36 | 37 | 38 | 39 | -------------------------------------------------------------------------------- /doc/install.txt: -------------------------------------------------------------------------------- 1 | Installation 2 | ============ 3 | 4 | 5 | Requirements 6 | ---------------------------------------- 7 | 8 | 9 | Work in progress... -------------------------------------------------------------------------------- /doc/introduction.txt: -------------------------------------------------------------------------------- 1 | Introduction 2 | ============ 3 | 4 | In progress... 5 | 6 | -------------------------------------------------------------------------------- /doc/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenSourceBrain/OpenCortex/ece5a5d1d773c4d993346087790795e5191818d6/doc/logo.png -------------------------------------------------------------------------------- /doc/modules.txt: -------------------------------------------------------------------------------- 1 | opencortex 2 | ========== 3 | 4 | .. toctree:: 5 | :maxdepth: 4 6 | 7 | opencortex 8 | -------------------------------------------------------------------------------- /doc/opencortex.build.txt: -------------------------------------------------------------------------------- 1 | opencortex build Package 2 | ======================== 3 | 4 | Utility functions for OpenCortex. See :mod:`opencortex.core` for the main module for user interaction with OpenCortex. 5 | 6 | :mod:`opencortex.build` Package 7 | ------------------------------- 8 | 9 | .. automodule:: opencortex.build 10 | :members: 11 | :undoc-members: 12 | :show-inheritance: 13 | 14 | 15 | 16 | -------------------------------------------------------------------------------- /doc/opencortex.core.txt: -------------------------------------------------------------------------------- 1 | opencortex core Package 2 | ======================= 3 | 4 | This is the main module for user interaction with OpenCortex. 5 | 6 | :mod:`opencortex.core` Package 7 | ------------------------------ 8 | 9 | .. automodule:: opencortex.core 10 | :members: 11 | :undoc-members: 12 | :show-inheritance: 13 | 14 | 15 | 16 | -------------------------------------------------------------------------------- /doc/opencortex.txt: -------------------------------------------------------------------------------- 1 | opencortex Package 2 | ================== 3 | 4 | See :mod:`opencortex.core` for the main module for user interaction with OpenCortex. 5 | 6 | :mod:`opencortex` Package 7 | ------------------------- 8 | 9 | .. automodule:: opencortex.__init__ 10 | :members: 11 | :undoc-members: 12 | :show-inheritance: 13 | 14 | Subpackages 15 | ----------- 16 | 17 | .. toctree:: 18 | 19 | opencortex.core 20 | opencortex.build 21 | opencortex.utils 22 | 23 | 24 | -------------------------------------------------------------------------------- /doc/opencortex.utils.txt: -------------------------------------------------------------------------------- 1 | opencortex utils Package 2 | ======================== 3 | 4 | 5 | Utility functions for OpenCortex. See :mod:`opencortex.core` for the main module for user interaction with OpenCortex. 6 | 7 | :mod:`opencortex.utils` Package 8 | ------------------------------- 9 | 10 | .. automodule:: opencortex.utils 11 | :members: 12 | :undoc-members: 13 | :show-inheritance: 14 | 15 | 16 | 17 | -------------------------------------------------------------------------------- /examples/ACNet/Ca_conc.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing intracellular Calcium dynamics, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /examples/ACNet/Kdr_bask.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the book Neuronal Networks of the Hippocampus, Traub and Miles 1991 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the book Neuronal Networks of the Hippocampus, Traub and Miles 1991 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., & Miles, R. (1991). Neuronal Networks of the Hippocampus. Cambridge University Press. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /examples/ACNet/Kdr_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /examples/ACNet/LeakConductance_bask.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for basket cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/ACNet/LeakConductance_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for pyramidal cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/ACNet/Na_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | Na channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /examples/Balanced/Ca_HVA.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | High voltage activated Ca2+ current. 9 | 10 | Comment from original mod file: 11 | Reuveni, Friedman, Amitai, and Gutnick, J.Neurosci. 1993 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 20 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | Calcium channels 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | -------------------------------------------------------------------------------- /examples/Balanced/Ih.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Non-specific cation current 9 | 10 | Comment from original mod file: 11 | Reference : : Kole,Hallermann,and Stuart, J. Neurosci. 2006 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 20 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | -------------------------------------------------------------------------------- /examples/Balanced/Im.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Muscarinic K+ current 9 | 10 | Comment from original mod file: 11 | :Reference : : Adams et al. 1982 - M-currents and other potassium currents in bullfrog sympathetic neurones 12 | :Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 21 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | K channels 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | -------------------------------------------------------------------------------- /examples/Balanced/Leak.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file containing a single Channel description 5 | 6 | 7 | 8 | ChannelML file containing a single Channel description 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/Balanced/NaTa_t.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Fast inactivating Na+ current 9 | 10 | Comment from original mod file: 11 | :Reference :Colbert and Pan 2002 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 20 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | Na channels 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | -------------------------------------------------------------------------------- /examples/Balanced/NaTs2_t.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Fast inactivating Na+ current. Comment from mod file (NaTs2_t.mod): took the NaTa and shifted both activation/inactivation by 6 mv 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, 17 | Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | Na channels 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | -------------------------------------------------------------------------------- /examples/Balanced/pas.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Simple example of a leak/passive conductance. Note: for GENESIS cells with a single leak conductance, 9 | it is better to use the Rm and Em variables for a passive current. 10 | 11 | 12 | 13 | 14 | -------------------------------------------------------------------------------- /examples/Balanced_24cells_336conns/Leak.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file containing a single Channel description 5 | 6 | 7 | 8 | ChannelML file containing a single Channel description 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/Complex/Ca_conc.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing intracellular Calcium dynamics, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /examples/Complex/Kdr_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /examples/Complex/LeakConductance_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for pyramidal cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/Complex/Na_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | Na channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /examples/Complex/RS.cell.nml: -------------------------------------------------------------------------------- 1 | 6 | 7 | 10 | 11 | -------------------------------------------------------------------------------- /examples/Complex/iaf.cell.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 7 | 8 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /examples/Complex/iafRef.cell.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 7 | 8 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/Deterministic/RS.cell.nml: -------------------------------------------------------------------------------- 1 | 6 | 7 | 10 | 11 | -------------------------------------------------------------------------------- /examples/Deterministic/iaf.cell.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 7 | 8 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /examples/GapJunctions/Ca_conc.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing intracellular Calcium dynamics, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /examples/GapJunctions/Kdr_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /examples/GapJunctions/LeakConductance_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for pyramidal cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/GapJunctions/Na_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | Na channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /examples/HDF5/ACNet.net.nml.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenSourceBrain/OpenCortex/ece5a5d1d773c4d993346087790795e5191818d6/examples/HDF5/ACNet.net.nml.h5 -------------------------------------------------------------------------------- /examples/HDF5/ACNet/Ca_conc.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing intracellular Calcium dynamics, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /examples/HDF5/ACNet/Kdr_bask.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the book Neuronal Networks of the Hippocampus, Traub and Miles 1991 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the book Neuronal Networks of the Hippocampus, Traub and Miles 1991 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., & Miles, R. (1991). Neuronal Networks of the Hippocampus. Cambridge University Press. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /examples/HDF5/ACNet/Kdr_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /examples/HDF5/ACNet/LeakConductance_bask.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for basket cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/HDF5/ACNet/LeakConductance_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for pyramidal cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/HDF5/ACNet/Na_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | Na channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /examples/HDF5/IClamps.net.nml.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenSourceBrain/OpenCortex/ece5a5d1d773c4d993346087790795e5191818d6/examples/HDF5/IClamps.net.nml.h5 -------------------------------------------------------------------------------- /examples/HDF5/IClamps/Ca_conc.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing intracellular Calcium dynamics, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /examples/HDF5/IClamps/Kdr_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /examples/HDF5/IClamps/LeakConductance_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for pyramidal cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/HDF5/IClamps/Na_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | Na channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /examples/HDF5/IClamps/RS.cell.nml: -------------------------------------------------------------------------------- 1 | 6 | 7 | 10 | 11 | -------------------------------------------------------------------------------- /examples/HDF5/LEMS_IClamps.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | -------------------------------------------------------------------------------- /examples/HDF5/SpikingNet.net.nml.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenSourceBrain/OpenCortex/ece5a5d1d773c4d993346087790795e5191818d6/examples/HDF5/SpikingNet.net.nml.h5 -------------------------------------------------------------------------------- /examples/HDF5/SpikingNet/RS.cell.nml: -------------------------------------------------------------------------------- 1 | 6 | 7 | 10 | 11 | -------------------------------------------------------------------------------- /examples/HDF5/Weights.net.nml.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/OpenSourceBrain/OpenCortex/ece5a5d1d773c4d993346087790795e5191818d6/examples/HDF5/Weights.net.nml.h5 -------------------------------------------------------------------------------- /examples/HDF5/Weights/Leak.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file containing a single Channel description 5 | 6 | 7 | 8 | ChannelML file containing a single Channel description 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/IClamps.net.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | This NeuroML 2 file was generated by OpenCortex v0.1.18 using: 5 | libNeuroML v0.6.3 6 | pyNeuroML v1.3.7 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | -------------------------------------------------------------------------------- /examples/IClamps/Ca_conc.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing intracellular Calcium dynamics, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /examples/IClamps/Kdr_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /examples/IClamps/LeakConductance_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for pyramidal cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/IClamps/Na_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | Na channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /examples/IClamps/RS.cell.nml: -------------------------------------------------------------------------------- 1 | 6 | 7 | 10 | 11 | -------------------------------------------------------------------------------- /examples/L23TraubDemo/cad__beta0_01__phi26000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /examples/L23TraubDemo/cad__beta0_02__phi260000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /examples/L23TraubDemo/cad__beta0_05__phi52000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /examples/L23TraubDemo/cad__beta0_05__phi520000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /examples/L23TraubDemo/pas.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file containing a single Channel description 5 | 6 | 7 | 8 | Simple passive leak conductance 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/LEMS_IClamps.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | -------------------------------------------------------------------------------- /examples/LEMS_SimpleNet.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | -------------------------------------------------------------------------------- /examples/Recording/Ca_conc.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file describing intracellular Calcium dynamics, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /examples/Recording/Kdr_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Potassium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | K channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /examples/Recording/LeakConductance_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Channel description 5 | 6 | 7 | 8 | Passive conductance for pyramidal cell 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/Recording/Na_pyr.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 5 | 6 | 7 | 8 | NeuroML file containing a single Sodium Channel description, from the Hippocampal CA3 neuron model presented in Traub et al., 1991. 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. Journal of neurophysiology, 66(2), 635-50. 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | Na channels 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /examples/SimpleNet.net.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | This NeuroML 2 file was generated by OpenCortex v0.1.18 using: 5 | libNeuroML v0.6.3 6 | pyNeuroML v1.3.7 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | -------------------------------------------------------------------------------- /examples/SimpleNet.py: -------------------------------------------------------------------------------- 1 | 2 | import opencortex.core as oc 3 | 4 | 5 | min_pop_size = 3 6 | 7 | def scale_pop_size(baseline, scale): 8 | return max(min_pop_size, int(baseline*scale)) 9 | 10 | 11 | 12 | def generate(reference = "SimpleNet", 13 | scale=1, 14 | format='xml'): 15 | 16 | population_size = scale_pop_size(3,scale) 17 | 18 | nml_doc, network = oc.generate_network(reference) 19 | 20 | oc.include_opencortex_cell(nml_doc, 'izhikevich/RS.cell.nml') 21 | 22 | pop = oc.add_population_in_rectangular_region(network, 23 | 'RS_pop', 24 | 'RS', 25 | population_size, 26 | 0,0,0, 27 | 100,100,100, 28 | color='0 .8 0') 29 | import neuroml 30 | pop.properties.append(neuroml.Property('radius',10)) 31 | 32 | syn = oc.add_exp_two_syn(nml_doc, 33 | id="syn0", 34 | gbase="2nS", 35 | erev="0mV", 36 | tau_rise="0.5ms", 37 | tau_decay="10ms") 38 | 39 | pfs = oc.add_poisson_firing_synapse(nml_doc, 40 | id="poissonFiringSyn", 41 | average_rate="50 Hz", 42 | synapse_id=syn.id) 43 | 44 | oc.add_inputs_to_population(network, 45 | "Stim0", 46 | pop, 47 | pfs.id, 48 | all_cells=True) 49 | 50 | nml_file_name = '%s.net.nml'%network.id 51 | oc.save_network(nml_doc, 52 | nml_file_name, 53 | validate=(format=='xml'), 54 | format = format) 55 | 56 | if format=='xml': 57 | oc.generate_lems_simulation(nml_doc, 58 | network, 59 | nml_file_name, 60 | duration = 500, 61 | dt = 0.025, 62 | report_file_name='report.simple.txt') 63 | 64 | 65 | if __name__ == '__main__': 66 | 67 | import sys 68 | 69 | if len(sys.argv)==2: 70 | generate(scale=int(sys.argv[1])) 71 | else: 72 | generate() -------------------------------------------------------------------------------- /examples/SimpleNet/RS.cell.nml: -------------------------------------------------------------------------------- 1 | 6 | 7 | 10 | 11 | -------------------------------------------------------------------------------- /examples/SpikingNet/RS.cell.nml: -------------------------------------------------------------------------------- 1 | 6 | 7 | 10 | 11 | -------------------------------------------------------------------------------- /examples/VClamp/cad__beta0_01__phi26000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /examples/VClamp/cad__beta0_05__phi52000.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 11 | 12 | 13 | 14 | ChannelML file based on Traub et al. 2003 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /examples/VClamp/pas.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file containing a single Channel description 5 | 6 | 7 | 8 | Simple passive leak conductance 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/Weights/Leak.channel.nml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ChannelML file containing a single Channel description 5 | 6 | 7 | 8 | ChannelML file containing a single Channel description 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /examples/neuromllite/LEMS_SimSimpleNet.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | -------------------------------------------------------------------------------- /examples/neuromllite/README.md: -------------------------------------------------------------------------------- 1 | Demonstrating use of NeuroMLlite to build similar models to the OpenCortex examples 2 | -------------------------------------------------------------------------------- /examples/neuromllite/SimSimpleNet.json: -------------------------------------------------------------------------------- 1 | { 2 | "SimSimpleNet": { 3 | "version": "NeuroMLlite v0.5.2", 4 | "network": "SimpleNet.json", 5 | "duration": 1000.0, 6 | "dt": 0.025, 7 | "record_traces": { 8 | "all": "*" 9 | } 10 | } 11 | } -------------------------------------------------------------------------------- /examples/neuromllite/SimpleNet.json: -------------------------------------------------------------------------------- 1 | { 2 | "SimpleNet": { 3 | "version": "NeuroMLlite v0.5.2", 4 | "notes": "Simple network with single population", 5 | "cells": { 6 | "RS": { 7 | "neuroml2_source_file": "../../NeuroML2/prototypes/izhikevich/RS.cell.nml" 8 | } 9 | }, 10 | "synapses": { 11 | "ampa": { 12 | "neuroml2_source_file": "../../NeuroML2/prototypes/synapses/ampa.synapse.nml" 13 | } 14 | }, 15 | "input_sources": { 16 | "poissonFiringSyn": { 17 | "parameters": { 18 | "average_rate": "50Hz", 19 | "synapse": "ampa", 20 | "spike_target": "./ampa" 21 | }, 22 | "neuroml2_input": "poissonFiringSynapse" 23 | } 24 | }, 25 | "regions": { 26 | "region1": { 27 | "x": 0.0, 28 | "y": 0.0, 29 | "z": 0.0, 30 | "width": 100.0, 31 | "height": 100.0, 32 | "depth": 100.0 33 | } 34 | }, 35 | "populations": { 36 | "RS_pop": { 37 | "size": 3, 38 | "component": "RS", 39 | "properties": { 40 | "color": "0 .8 0" 41 | }, 42 | "random_layout": { 43 | "region": "region1" 44 | } 45 | } 46 | }, 47 | "inputs": { 48 | "Stim0": { 49 | "input_source": "poissonFiringSyn", 50 | "population": "RS_pop", 51 | "percentage": 100.0 52 | } 53 | }, 54 | "temperature": 32.0 55 | } 56 | } -------------------------------------------------------------------------------- /examples/neuromllite/SimpleNet.net.nml: -------------------------------------------------------------------------------- 1 | 2 | Generated by NeuroMLlite v0.5.2 3 | Generated network: SimpleNet 4 | Generation seed: 1234 5 | 6 | 7 | 8 | 9 | Simple network with single population 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | -------------------------------------------------------------------------------- /examples/neuromllite/SimpleNet.py: -------------------------------------------------------------------------------- 1 | from neuromllite import Network, Cell, InputSource, Population, Synapse, RectangularRegion, RandomLayout 2 | from neuromllite import Projection, RandomConnectivity, Input, Simulation 3 | from neuromllite.NetworkGenerator import generate_and_run 4 | import sys 5 | 6 | ################################################################################ 7 | ### Build new network 8 | 9 | net = Network(id='SimpleNet') 10 | net.notes = 'Simple network with single population' 11 | net.temperature = 32.0 12 | 13 | cell = Cell(id='RS', neuroml2_source_file='../../NeuroML2/prototypes/izhikevich/RS.cell.nml') 14 | net.cells.append(cell) 15 | 16 | syn = Synapse(id='ampa', neuroml2_source_file='../../NeuroML2/prototypes/synapses/ampa.synapse.nml') 17 | net.synapses.append(syn) 18 | 19 | input_source = InputSource(id='poissonFiringSyn', 20 | neuroml2_input='poissonFiringSynapse', 21 | parameters={'average_rate':"50Hz", 'synapse':syn.id, 'spike_target':"./ampa"}) 22 | net.input_sources.append(input_source) 23 | 24 | r1 = RectangularRegion(id='region1', x=0,y=0,z=0,width=100,height=100,depth=100) 25 | net.regions.append(r1) 26 | 27 | p0 = Population(id='RS_pop', size=3, component=cell.id, properties={'color':'0 .8 0'},random_layout = RandomLayout(region=r1.id)) 28 | 29 | net.populations.append(p0) 30 | 31 | net.inputs.append(Input(id='Stim0', 32 | input_source=input_source.id, 33 | population=p0.id, 34 | percentage=100)) 35 | 36 | print(net.to_json()) 37 | new_file = net.to_json_file('%s.json'%net.id) 38 | 39 | 40 | ################################################################################ 41 | ### Build Simulation object & save as JSON 42 | 43 | sim = Simulation(id='SimSimpleNet', 44 | network=new_file, 45 | duration='1000', 46 | dt='0.025', 47 | record_traces={'all':'*'}) 48 | 49 | sim.to_json_file() 50 | 51 | 52 | 53 | ################################################################################ 54 | ### Run in some simulators 55 | 56 | from neuromllite.NetworkGenerator import check_to_generate_or_run 57 | import sys 58 | 59 | check_to_generate_or_run(sys.argv, sim) 60 | -------------------------------------------------------------------------------- /examples/redo.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -ex 3 | cd .. 4 | pip install . 5 | cd opencortex/test 6 | pytest -vs 7 | cd ../../examples 8 | 9 | cleanGenNML.sh 10 | ./regenerateAll.sh 11 | 12 | time omv all -V 13 | date 14 | 15 | echo "Finished OpenCortex tests" 16 | -------------------------------------------------------------------------------- /examples/regenerateAll.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | python Complex.py 4 | python IClamps.py 5 | python SpikingNet.py 6 | python SimpleNet.py 7 | python Deterministic.py 8 | python Recording.py 9 | python L23TraubDemo.py 10 | python ACNet.py 11 | python GapJunctions.py 12 | python Weights.py 13 | python VClamp.py 14 | 15 | python Balanced.py -all 16 | 17 | -------------------------------------------------------------------------------- /examples/test_scaling.py: -------------------------------------------------------------------------------- 1 | from Balanced import generate 2 | 3 | 4 | nml_doc, nml_file_name, lems_file_name = generate(num_bbp =10, 5 | scalePops = 20, 6 | scalex=2, 7 | scalez=2, 8 | connections=True, 9 | format='hdf5') 10 | 11 | from neuroml.loaders import NeuroMLHdf5Loader 12 | 13 | nml_doc2 = NeuroMLHdf5Loader.load(nml_file_name) 14 | 15 | 16 | for doc in [nml_doc,nml_doc2]: 17 | doc.summary() 18 | -------------------------------------------------------------------------------- /examples/tests/.test.acnet.h5.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../HDF5/LEMS_ACNet.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.acnet.mep 6 | experiments: 7 | p_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../HDF5/Sim_ACNet.pop_pyr.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 1.8686199494019725e-16 18 | p_1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../HDF5/Sim_ACNet.pop_pyr.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 2.267388067842362e-16 29 | b_0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../HDF5/Sim_ACNet.pop_bask.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 2.14220534617705e-16 40 | -------------------------------------------------------------------------------- /examples/tests/.test.acnet.h5.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../HDF5/LEMS_ACNet.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.acnet.mep 6 | experiments: 7 | p_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../HDF5/Sim_ACNet.pop_pyr.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0.0 18 | p_1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../HDF5/Sim_ACNet.pop_pyr.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0.000 29 | b_0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../HDF5/Sim_ACNet.pop_bask.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 0.000 40 | 41 | -------------------------------------------------------------------------------- /examples/tests/.test.acnet.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_ACNet.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.acnet.mep 6 | experiments: 7 | p_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_ACNet.pop_pyr.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 1.8686199494019725e-16 18 | p_1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_ACNet.pop_pyr.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 2.267388067842362e-16 29 | b_0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_ACNet.pop_bask.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 2.14220534617705e-16 40 | -------------------------------------------------------------------------------- /examples/tests/.test.acnet.jnmlnetpyne4.omt_: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_ACNet.xml 4 | engine: jNeuroML_NetPyNE_NP4 5 | mep: .test.acnet.mep 6 | experiments: 7 | p_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_ACNet.pop_pyr.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 1.8686199494019725e-16 18 | p_1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_ACNet.pop_pyr.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 2.267388067842362e-16 29 | b_0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_ACNet.pop_bask.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 2.14220534617705e-16 40 | -------------------------------------------------------------------------------- /examples/tests/.test.acnet.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_ACNet.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.acnet.mep 6 | experiments: 7 | p_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_ACNet.pop_pyr.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0.0 18 | p_1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_ACNet.pop_pyr.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0.000 29 | b_0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_ACNet.pop_bask.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 0.000 40 | 41 | -------------------------------------------------------------------------------- /examples/tests/.test.acnet.mep: -------------------------------------------------------------------------------- 1 | system: Test 2 | experiments: 3 | p_0: 4 | expected: 5 | spike times: [14.475, 38.025000000000006, 54.15, 70.75, 77.075, 83.1, 90.075, 107.14999999999999, 123.5, 134.35, 141.225, 146.85, 152.2, 166.17499999999998, 175.675, 191.625, 197.3, 204.375, 214.2, 222.125, 239.1, 246.775] 6 | p_1: 7 | expected: 8 | spike times: [3.8, 10.4, 16.85, 36.5, 49.325, 58.35, 90.85, 97.775, 104.25, 110.7, 118.8, 125.35, 132.97500000000002, 142.95, 150.15, 157.4, 166.125, 177.75, 184.5, 191.325, 198.075, 205.725, 215.95000000000002, 223.6, 230.75] 9 | b_0: 10 | expected: 11 | spike times: [21.6, 39.65, 56.275, 70.6, 84.025, 100.55, 116.95, 132.67499999999998, 149.725, 165.9, 180.5, 196.55, 212.07500000000002, 226.92499999999998, 242.65] 12 | -------------------------------------------------------------------------------- /examples/tests/.test.balanced.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_Balanced.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.balanced.mep 6 | experiments: 7 | pe_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../popExc_v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0.0 18 | pi_0: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../popInh_v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0.0 29 | pb_0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../popBBP_v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 0.00026371308016883636 40 | -------------------------------------------------------------------------------- /examples/tests/.test.balanced.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_Balanced.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.balanced.mep 6 | experiments: 7 | pe_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../popExc_v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0.0 18 | pi_0: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../popInh_v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0.0 29 | pb_0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../popBBP_v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 0.0 40 | -------------------------------------------------------------------------------- /examples/tests/.test.balanced.mep: -------------------------------------------------------------------------------- 1 | system: Test 2 | experiments: 3 | pe_0: 4 | expected: 5 | spike times: [99.225, 258.575, 425.575, 586.625, 752.0, 761.525, 936.975] 6 | pi_0: 7 | expected: 8 | spike times: [99.575, 264.79999999999995, 428.05, 585.625, 760.575, 940.5250000000001] 9 | pb_0: 10 | expected: 11 | spike times: [98.025, 264.25, 429.975, 588.975, 762.025, 942.1500000000001] -------------------------------------------------------------------------------- /examples/tests/.test.complex.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_Complex.xml 4 | engine: jNeuroML_NetPyNE 5 | -------------------------------------------------------------------------------- /examples/tests/.test.complex.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_Complex.xml 4 | engine: jNeuroML_NEURON 5 | -------------------------------------------------------------------------------- /examples/tests/.test.deterministic.jnml.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_Deterministic.xml 4 | engine: jNeuroML 5 | mep: .test.deterministic.mep 6 | experiments: 7 | 1_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_Deterministic.pop_rs.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0.000806 -------------------------------------------------------------------------------- /examples/tests/.test.deterministic.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_Deterministic.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.deterministic.mep 6 | experiments: 7 | 1_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_Deterministic.pop_rs.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 -------------------------------------------------------------------------------- /examples/tests/.test.deterministic.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_Deterministic.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.deterministic.mep 6 | experiments: 7 | 1_0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_Deterministic.pop_rs.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 -------------------------------------------------------------------------------- /examples/tests/.test.deterministic.mep: -------------------------------------------------------------------------------- 1 | system: Test 2 | experiments: 3 | 1_0: 4 | expected: 5 | spike times: [82.64, 104.2, 130.425, 157.95999999999998, 185.185, 213.345, 241.62, 269.76, 297.53499999999997, 324.85999999999996, 353.03499999999997, 381.03999999999996, 408.735, 435.78499999999997, 471.52] -------------------------------------------------------------------------------- /examples/tests/.test.iclamps.h5.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../HDF5/LEMS_IClamps.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.iclamps.mep 6 | experiments: 7 | izh: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../HDF5/Sim_IClamps.popIzh.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | hh: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../HDF5/Sim_IClamps.popHH.v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | -------------------------------------------------------------------------------- /examples/tests/.test.iclamps.h5.jnmlnetpyne2.omt_: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../HDF5/LEMS_IClamps.xml 4 | engine: jNeuroML_NetPyNE_NP2 5 | mep: .test.iclamps.mep 6 | experiments: 7 | izh: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../HDF5/Sim_IClamps.popIzh.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | hh: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../HDF5/Sim_IClamps.popHH.v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | -------------------------------------------------------------------------------- /examples/tests/.test.iclamps.h5.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../HDF5/LEMS_IClamps.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.iclamps.mep 6 | experiments: 7 | izh: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../HDF5/Sim_IClamps.popIzh.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | hh: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../HDF5/Sim_IClamps.popHH.v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | -------------------------------------------------------------------------------- /examples/tests/.test.iclamps.jnml.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_IClamps.xml 4 | engine: jNeuroML 5 | mep: .test.iclamps.mep 6 | experiments: 7 | izh: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_IClamps.popIzh.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0.0003164 18 | hh: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_IClamps.popHH.v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0.0046 29 | -------------------------------------------------------------------------------- /examples/tests/.test.iclamps.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_IClamps.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.iclamps.mep 6 | experiments: 7 | izh: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_IClamps.popIzh.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | hh: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_IClamps.popHH.v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 -------------------------------------------------------------------------------- /examples/tests/.test.iclamps.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_IClamps.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.iclamps.mep 6 | experiments: 7 | izh: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_IClamps.popIzh.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | hh: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_IClamps.popHH.v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 -------------------------------------------------------------------------------- /examples/tests/.test.iclamps.jnmlpynnnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_IClamps.xml 4 | engine: jNeuroML_PyNN_NEURON 5 | -------------------------------------------------------------------------------- /examples/tests/.test.iclamps.mep: -------------------------------------------------------------------------------- 1 | system: Test 2 | experiments: 3 | izh: 4 | expected: 5 | spike times: [108.58500000000001, 116.895, 126.15, 136.26, 147.04, 158.28, 169.81, 181.5, 193.28, 205.10999999999999, 216.965, 228.825, 240.7, 252.575, 264.455, 276.33000000000004, 288.21000000000004, 300.09000000000003, 311.97, 323.85, 335.72999999999996, 347.60999999999996, 359.48999999999995, 371.37, 383.25, 395.13] 6 | 7 | hh: 8 | expected: 9 | spike times: [101.045, 111.8, 123.19, 135.285, 148.03, 161.265, 174.82500000000002, 188.595, 202.505, 216.505, 230.57, 244.685, 258.84000000000003, 273.02500000000003, 287.235, 301.465, 315.70500000000004, 329.96, 344.22, 358.48999999999995, 372.76, 387.035] 10 | -------------------------------------------------------------------------------- /examples/tests/.test.l23traub.mep: -------------------------------------------------------------------------------- 1 | system: Test Simple 2 | experiments: 3 | rs0: 4 | expected: 5 | spike times: [ 7.6, 106.14999999999999, 277.225 ] 6 | bask0: 7 | expected: 8 | spike times: [2.975, 89.27499999999999, 174.57500000000002, 264.6] -------------------------------------------------------------------------------- /examples/tests/.test.l23traubdemo.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_L23TraubDemo.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.l23traub.mep 6 | experiments: 7 | rs0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_L23TraubDemo.pop_rs.0.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | bask0: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_L23TraubDemo.pop_bask.0.v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 -------------------------------------------------------------------------------- /examples/tests/.test.l23traubdemo.jnmlnetpyne2.omt_: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_L23TraubDemo.xml 4 | engine: jNeuroML_NetPyNE_NP2 5 | mep: .test.l23traub.mep 6 | experiments: 7 | rs0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_L23TraubDemo.pop_rs.0.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | bask0: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_L23TraubDemo.pop_bask.0.v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 -------------------------------------------------------------------------------- /examples/tests/.test.l23traubdemo.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_L23TraubDemo.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.l23traub.mep 6 | experiments: 7 | rs0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_L23TraubDemo.pop_rs.0.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | bask0: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_L23TraubDemo.pop_bask.0.v.dat 23 | columns: [0,1] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 -------------------------------------------------------------------------------- /examples/tests/.test.recording.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_Recording.xml 4 | engine: jNeuroML_NEURON 5 | -------------------------------------------------------------------------------- /examples/tests/.test.simple.jnml.mep: -------------------------------------------------------------------------------- 1 | system: Test Simple 2 | experiments: 3 | 0: 4 | expected: 5 | spike times: [271.0, 295.175, 360.025] 6 | 1: 7 | expected: 8 | spike times: [92.35000000000001, 228.775, 406.875] 9 | 2: 10 | expected: 11 | spike times: [80.22500000000001, 186.29999999999998, 295.35, 378.3, 483.425] 12 | 13 | -------------------------------------------------------------------------------- /examples/tests/.test.simple.jnml.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SimpleNet.xml 4 | engine: jNeuroML 5 | mep: .test.simple.jnml.mep 6 | experiments: 7 | 0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_SimpleNet.RS_pop.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | 1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_SimpleNet.RS_pop.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | 2: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_SimpleNet.RS_pop.v.dat 34 | columns: [0,3] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 0 40 | -------------------------------------------------------------------------------- /examples/tests/.test.simple.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SimpleNet.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.simple.jnmlnrn.mep 6 | experiments: 7 | 0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_SimpleNet.RS_pop.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 5e-8 18 | 1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_SimpleNet.RS_pop.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 5e-8 29 | 2: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_SimpleNet.RS_pop.v.dat 34 | columns: [0,3] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 5e-8 40 | -------------------------------------------------------------------------------- /examples/tests/.test.simple.jnmlnetpyne2.omt_: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SimpleNet.xml 4 | engine: jNeuroML_NetPyNE_NP2 5 | mep: .test.simple.jnmlnrn.mep 6 | experiments: 7 | 0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_SimpleNet.RS_pop.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 5e-8 18 | 1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_SimpleNet.RS_pop.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 5e-8 29 | 2: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_SimpleNet.RS_pop.v.dat 34 | columns: [0,3] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 5e-8 40 | -------------------------------------------------------------------------------- /examples/tests/.test.simple.jnmlnrn.mep: -------------------------------------------------------------------------------- 1 | system: Test Simple 2 | experiments: 3 | 0: 4 | expected: 5 | spike times: [86.275, 323.975] 6 | 1: 7 | expected: 8 | spike times: [27.275000000000002, 103.35, 157.45000000000002, 311.95, 390.25] 9 | 2: 10 | expected: 11 | spike times: [89.725, 223.35, 245.625, 340.55] -------------------------------------------------------------------------------- /examples/tests/.test.simple.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SimpleNet.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.simple.jnmlnrn.mep 6 | experiments: 7 | 0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_SimpleNet.RS_pop.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | 1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_SimpleNet.RS_pop.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | 2: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_SimpleNet.RS_pop.v.dat 34 | columns: [0,3] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: 0 39 | tolerance: 0 40 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.h5.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../HDF5/LEMS_SpikingNet.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.spiking.nrn.mep 6 | experiments: 7 | pr0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../HDF5/Sim_SpikingNet.pop_pre.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 1.04e-5 18 | pr1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../HDF5/Sim_SpikingNet.pop_pre.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | po0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../HDF5/Sim_SpikingNet.pop_post.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: -59.5 39 | tolerance: 1.41e-5 40 | po1: 41 | observables: 42 | spike times: 43 | file: 44 | path: ../HDF5/Sim_SpikingNet.pop_post.v.dat 45 | columns: [0,2] 46 | scaling: [1000, 1000] 47 | spike detection: 48 | method: threshold 49 | threshold: -59.8 50 | tolerance: 5.38198649122e-05 51 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.h5.jnmlnetpyne2.omt_: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../HDF5/LEMS_SpikingNet.xml 4 | engine: jNeuroML_NetPyNE_NP2 5 | mep: .test.spiking.nrn.mep 6 | experiments: 7 | pr0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../HDF5/Sim_SpikingNet.pop_pre.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 1.04e-5 18 | pr1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../HDF5/Sim_SpikingNet.pop_pre.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | po0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../HDF5/Sim_SpikingNet.pop_post.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: -59.5 39 | tolerance: 1.41e-5 40 | po1: 41 | observables: 42 | spike times: 43 | file: 44 | path: ../HDF5/Sim_SpikingNet.pop_post.v.dat 45 | columns: [0,2] 46 | scaling: [1000, 1000] 47 | spike detection: 48 | method: threshold 49 | threshold: -59.8 50 | tolerance: 5.38198649122e-05 51 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.h5.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../HDF5/LEMS_SpikingNet.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.spiking.nrn.mep 6 | experiments: 7 | pr0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../HDF5/Sim_SpikingNet.pop_pre.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | pr1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../HDF5/Sim_SpikingNet.pop_pre.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | po0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../HDF5/Sim_SpikingNet.pop_post.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: -59.5 39 | tolerance: 0 40 | po1: 41 | observables: 42 | spike times: 43 | file: 44 | path: ../HDF5/Sim_SpikingNet.pop_post.v.dat 45 | columns: [0,2] 46 | scaling: [1000, 1000] 47 | spike detection: 48 | method: threshold 49 | threshold: -59.8 50 | tolerance: 0 51 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.jnml.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SpikingNet.xml 4 | engine: jNeuroML 5 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.jnmlnetpyne.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SpikingNet.xml 4 | engine: jNeuroML_NetPyNE 5 | mep: .test.spiking.nrn.mep 6 | experiments: 7 | pr0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_SpikingNet.pop_pre.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | pr1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_SpikingNet.pop_pre.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | po0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_SpikingNet.pop_post.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: -59.5 39 | tolerance: 1.2103899876666817e-05 40 | po1: 41 | observables: 42 | spike times: 43 | file: 44 | path: ../Sim_SpikingNet.pop_post.v.dat 45 | columns: [0,2] 46 | scaling: [1000, 1000] 47 | spike detection: 48 | method: threshold 49 | threshold: -59.8 50 | tolerance: 1.8751523561059118e-05 51 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.jnmlnetpyne2.omt_: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SpikingNet.xml 4 | engine: jNeuroML_NetPyNE_NP2 5 | mep: .test.spiking.nrn.mep 6 | experiments: 7 | pr0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_SpikingNet.pop_pre.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | pr1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_SpikingNet.pop_pre.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | po0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_SpikingNet.pop_post.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: -59.5 39 | tolerance: 1.2103899876666817e-05 40 | po1: 41 | observables: 42 | spike times: 43 | file: 44 | path: ../Sim_SpikingNet.pop_post.v.dat 45 | columns: [0,2] 46 | scaling: [1000, 1000] 47 | spike detection: 48 | method: threshold 49 | threshold: -59.8 50 | tolerance: 1.8751523561059118e-05 51 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.jnmlnetpyne4.omt_: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SpikingNet.xml 4 | engine: jNeuroML_NetPyNE_NP4 5 | mep: .test.spiking.nrn.mep 6 | experiments: 7 | pr0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_SpikingNet.pop_pre.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | pr1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_SpikingNet.pop_pre.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | po0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_SpikingNet.pop_post.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: -59.5 39 | tolerance: 1.2103899876666817e-05 40 | po1: 41 | observables: 42 | spike times: 43 | file: 44 | path: ../Sim_SpikingNet.pop_post.v.dat 45 | columns: [0,2] 46 | scaling: [1000, 1000] 47 | spike detection: 48 | method: threshold 49 | threshold: -59.8 50 | tolerance: 1.8751523561059118e-05 51 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SpikingNet.xml 4 | engine: jNeuroML_NEURON 5 | mep: .test.spiking.nrn.mep 6 | experiments: 7 | pr0: 8 | observables: 9 | spike times: 10 | file: 11 | path: ../Sim_SpikingNet.pop_pre.v.dat 12 | columns: [0,1] 13 | scaling: [1000, 1000] 14 | spike detection: 15 | method: threshold 16 | threshold: 0 17 | tolerance: 0 18 | pr1: 19 | observables: 20 | spike times: 21 | file: 22 | path: ../Sim_SpikingNet.pop_pre.v.dat 23 | columns: [0,2] 24 | scaling: [1000, 1000] 25 | spike detection: 26 | method: threshold 27 | threshold: 0 28 | tolerance: 0 29 | po0: 30 | observables: 31 | spike times: 32 | file: 33 | path: ../Sim_SpikingNet.pop_post.v.dat 34 | columns: [0,1] 35 | scaling: [1000, 1000] 36 | spike detection: 37 | method: threshold 38 | threshold: -59.5 39 | tolerance: 0 40 | po1: 41 | observables: 42 | spike times: 43 | file: 44 | path: ../Sim_SpikingNet.pop_post.v.dat 45 | columns: [0,2] 46 | scaling: [1000, 1000] 47 | spike detection: 48 | method: threshold 49 | threshold: -59.8 50 | tolerance: 0 51 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.jnmlpynnnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_SpikingNet.xml 4 | engine: jNeuroML_PyNN_NEURON 5 | -------------------------------------------------------------------------------- /examples/tests/.test.spiking.nrn.mep: -------------------------------------------------------------------------------- 1 | system: Test Simple 2 | experiments: 3 | pr0: 4 | expected: 5 | spike times: [125.66, 224.47, 287.59000000000003, 427.03999999999996, 626.51, 726.35, 764.51, 814.94, 862.72, 940.23] 6 | pr1: 7 | expected: 8 | spike times: [31.61, 78.14, 146.68, 259.18, 324.40999999999997, 448.07000000000005, 540.54, 612.3100000000001, 670.8, 766.98, 805.87, 866.88, 952.08] 9 | 10 | po0: 11 | expected: 12 | spike times: [132.67000000000002, 172.24, 184.91, 202.2, 260.85999999999996, 277.38, 292.65000000000003, 395.06, 430.05, 462.59, 621.49, 659.85, 733.12, 826.1800000000001, 951.85] 13 | po1: 14 | expected: 15 | spike times: [101.43, 151.75, 255.38, 337.82, 399.27000000000004, 533.2900000000001, 603.37, 676.87, 775.78, 823.4200000000001, 913.18] -------------------------------------------------------------------------------- /examples/tests/.test.validate.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | # This test will validate all of the NeuroML 2 files in the current directory using: jnml -validate *.nml 4 | target: "../*.nml" 5 | engine: jNeuroML_validate 6 | -------------------------------------------------------------------------------- /examples/tests/.test.vclamp.jnmlnrn.omt: -------------------------------------------------------------------------------- 1 | # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 2 | 3 | target: ../LEMS_VClamp.xml 4 | engine: jNeuroML_NEURON 5 | -------------------------------------------------------------------------------- /opencortex/__init__.py: -------------------------------------------------------------------------------- 1 | ############################################################### 2 | ### 3 | ### Note: OpenCortex is under active development, the API is subject to change without notice!! 4 | ### 5 | ### Authors: Padraig Gleeson, Rokas Stanislovas 6 | ### 7 | ### This software has been funded by the Wellcome Trust, as well as a GSoC 2016 project 8 | ### on Cortical Network develoment 9 | ### 10 | ############################################################## 11 | 12 | print("\n*********************************************************************************************") 13 | print(" Please note that OpenCortex is in a preliminary state "); 14 | print(" and the API is subject to change without notice! ") 15 | print("*********************************************************************************************\n") 16 | 17 | __version__ = '0.1.18' 18 | 19 | 20 | verbose = False 21 | 22 | def print_comment_v(text): 23 | """ 24 | Always print the comment 25 | """ 26 | print_comment(text, True) 27 | 28 | 29 | def print_comment(text, print_it=verbose): 30 | """ 31 | Print a comment only if print_it == True 32 | """ 33 | prefix = "OpenCortex >>> " 34 | if not isinstance(text, str): text = text.decode('ascii') 35 | if print_it: 36 | 37 | print("%s%s"%(prefix, text.replace("\n", "\n"+prefix))) 38 | 39 | def set_verbose(value=True): 40 | global verbose 41 | verbose = value 42 | -------------------------------------------------------------------------------- /opencortex/test/README.md: -------------------------------------------------------------------------------- 1 | Some tests... -------------------------------------------------------------------------------- /opencortex/utils/color.py: -------------------------------------------------------------------------------- 1 | ############################################################### 2 | ### 3 | ### Note: OpenCortex is under active development, the API is subject to change without notice!! 4 | ### 5 | ### Authors: Padraig Gleeson, Rokas Stanislovas 6 | ### 7 | ### This software has been funded by the Wellcome Trust, as well as a GSoC 2016 project 8 | ### on Cortical Network develoment 9 | ### 10 | ############################################################## 11 | 12 | # Colors to use across different networks to ease visual comparison 13 | 14 | # Reds 15 | L1_PRINCIPAL_CELL = '0.4 0.6 0.6' # rgb(95,158,160) 16 | L1_INTERNEURON = '0.5 0.9 0.9' # rgb(175,238,238) 17 | 18 | # Reds 19 | L23_PRINCIPAL_CELL = '0.6 0 0' 20 | L23_PRINCIPAL_CELL_2 = '1 0.2 0.2' 21 | L23_INTERNEURON = '1 0.4 0.4' 22 | L23_INTERNEURON_2 = '1 0.6 0.6' 23 | 24 | # Blues 25 | L4_PRINCIPAL_CELL = '0 0.25 0.5' 26 | L4_INTERNEURON = '0.8 0.7 1' 27 | 28 | #Greens 29 | L5_PRINCIPAL_CELL = '0 0.4 0' 30 | L5_PRINCIPAL_CELL_2 = '0 0.8 0.4' 31 | L5_INTERNEURON = '0.8 1 0.8' 32 | L5_INTERNEURON_2 = '0.8 1 0.6' 33 | 34 | #Oranges 35 | L6_PRINCIPAL_CELL = '1 0.6 0.2' 36 | L6_INTERNEURON = '1 0.8 0.8' 37 | 38 | 39 | THALAMUS_1 = '0 0.6 0.6' #Cyan 40 | THALAMUS_2 = '1 0.6 0.6' #Olive 41 | 42 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | import opencortex 4 | version = opencortex.__version__ 5 | 6 | setup( 7 | name='OpenCortex', 8 | version=version, 9 | author='Rokas Stanislovas and Padraig Gleeson', 10 | author_email='p.gleeson@gmail.com', 11 | packages = ['opencortex', 12 | 'opencortex.core', 13 | 'opencortex.build', 14 | 'opencortex.test', 15 | 'opencortex.utils'], 16 | package_data={ 17 | 'opencortex': [ 18 | '../NeuroML2/prototypes/iaf/*', 19 | '../NeuroML2/prototypes/izhikevich/*', 20 | '../NeuroML2/prototypes/Thalamocortical/*', 21 | '../NeuroML2/prototypes/BlueBrainProject_NMC/*', 22 | '../NeuroML2/prototypes/AllenInstituteCellTypesDB_HH/*', 23 | '../NeuroML2/prototypes/L23Pyr_SmithEtAl2013/*', 24 | '../NeuroML2/prototypes/acnet2/*']}, 25 | 26 | url='https://github.com/OpenSourceBrain/OpenCortex', 27 | license='LICENSE.lesser', 28 | description='A framework for building cortical network models', 29 | long_description=open('README.md').read(), 30 | install_requires=[ 31 | 'pyNeuroML>=0.3.18', # sets dependencies for other neuroml libs 32 | 'matplotlib', 33 | 'tables'], 34 | dependency_links=[ 35 | 'git+https://github.com/NeuralEnsemble/libNeuroML.git@development#egg=libNeuroML-0.2.10' 36 | ], 37 | classifiers = [ 38 | 'Intended Audience :: Science/Research', 39 | 'License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)', 40 | 'Natural Language :: English', 41 | 'Operating System :: OS Independent', 42 | 'Programming Language :: Python :: 2.6', 43 | 'Programming Language :: Python :: 2.7', 44 | 'Programming Language :: Python :: 3.2', 45 | 'Topic :: Scientific/Engineering'] 46 | ) 47 | --------------------------------------------------------------------------------