Adding files from github

CITATION.md

@@ -0,0 +1,13 @@
+## Citing this model
+
+The software in this repository is provided under the terms of the [software license](LICENSE) included with it. 
+
+If you use this model in your research, we respectfully ask you to cite:
+
+**The original publication upon which this model is based**
+
+   - Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology 1952.
+
+**The latest release of this repository**
+
+   - This link should provide a DOI/citation for the latest version released: https://www.zenodo.org/badge/latestdoi/27819667. If you would like us to make a new release, please [open an issue](../../issues). 

LICENSE

@@ -0,0 +1,166 @@
+GNU LESSER GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+
+  This version of the GNU Lesser General Public License incorporates
+the terms and conditions of version 3 of the GNU General Public
+License, supplemented by the additional permissions listed below.
+
+  0. Additional Definitions.
+
+  As used herein, "this License" refers to version 3 of the GNU Lesser
+General Public License, and the "GNU GPL" refers to version 3 of the GNU
+General Public License.
+
+  "The Library" refers to a covered work governed by this License,
+other than an Application or a Combined Work as defined below.
+
+  An "Application" is any work that makes use of an interface provided
+by the Library, but which is not otherwise based on the Library.
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+Application, but excluding the System Libraries of the Combined Work.
+
+  1. Exception to Section 3 of the GNU GPL.
+
+  You may convey a covered work under sections 3 and 4 of this License
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+  2. Conveying Modified Versions.
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+  If you modify a copy of the Library, and, in your modifications, a
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+   b) under the GNU GPL, with none of the additional permissions of
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+  3. Object Code Incorporating Material from Library Header Files.
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+   b) Give prominent notice with the combined library that part of it
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+  6. Revised Versions of the GNU Lesser General Public License.
+
+  The Free Software Foundation may publish revised and/or new versions
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+Library.
+

Tutorial/Makefile

@@ -0,0 +1,177 @@
+# Makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+PAPER         =
+BUILDDIR      = _build
+
+# User-friendly check for sphinx-build
+ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
+$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
+endif
+
+# Internal variables.
+PAPEROPT_a4     = -D latex_paper_size=a4
+PAPEROPT_letter = -D latex_paper_size=letter
+ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+# the i18n builder cannot share the environment and doctrees with the others
+I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+
+.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
+
+help:
+	@echo "Please use \`make <target>' where <target> is one of"
+	@echo "  html       to make standalone HTML files"
+	@echo "  dirhtml    to make HTML files named index.html in directories"
+	@echo "  singlehtml to make a single large HTML file"
+	@echo "  pickle     to make pickle files"
+	@echo "  json       to make JSON files"
+	@echo "  htmlhelp   to make HTML files and a HTML help project"
+	@echo "  qthelp     to make HTML files and a qthelp project"
+	@echo "  devhelp    to make HTML files and a Devhelp project"
+	@echo "  epub       to make an epub"
+	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
+	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
+	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
+	@echo "  text       to make text files"
+	@echo "  man        to make manual pages"
+	@echo "  texinfo    to make Texinfo files"
+	@echo "  info       to make Texinfo files and run them through makeinfo"
+	@echo "  gettext    to make PO message catalogs"
+	@echo "  changes    to make an overview of all changed/added/deprecated items"
+	@echo "  xml        to make Docutils-native XML files"
+	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
+	@echo "  linkcheck  to check all external links for integrity"
+	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
+
+clean:
+	rm -rf $(BUILDDIR)/*
+
+html:
+	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+dirhtml:
+	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
+
+singlehtml:
+	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
+	@echo
+	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
+
+pickle:
+	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
+	@echo
+	@echo "Build finished; now you can process the pickle files."
+
+json:
+	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
+	@echo
+	@echo "Build finished; now you can process the JSON files."
+
+htmlhelp:
+	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
+	@echo
+	@echo "Build finished; now you can run HTML Help Workshop with the" \
+	      ".hhp project file in $(BUILDDIR)/htmlhelp."
+
+qthelp:
+	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
+	@echo
+	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
+	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
+	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/HodginHuxleyLEMSTutorial.qhcp"
+	@echo "To view the help file:"
+	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/HodginHuxleyLEMSTutorial.qhc"
+
+devhelp:
+	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
+	@echo
+	@echo "Build finished."
+	@echo "To view the help file:"
+	@echo "# mkdir -p $$HOME/.local/share/devhelp/HodginHuxleyLEMSTutorial"
+	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/HodginHuxleyLEMSTutorial"
+	@echo "# devhelp"
+
+epub:
+	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
+	@echo
+	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
+
+latex:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo
+	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
+	@echo "Run \`make' in that directory to run these through (pdf)latex" \
+	      "(use \`make latexpdf' here to do that automatically)."
+
+latexpdf:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through pdflatex..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+latexpdfja:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through platex and dvipdfmx..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+text:
+	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
+	@echo
+	@echo "Build finished. The text files are in $(BUILDDIR)/text."
+
+man:
+	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
+	@echo
+	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
+
+texinfo:
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo
+	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
+	@echo "Run \`make' in that directory to run these through makeinfo" \
+	      "(use \`make info' here to do that automatically)."
+
+info:
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo "Running Texinfo files through makeinfo..."
+	make -C $(BUILDDIR)/texinfo info
+	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
+
+gettext:
+	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
+	@echo
+	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
+
+changes:
+	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
+	@echo
+	@echo "The overview file is in $(BUILDDIR)/changes."
+
+linkcheck:
+	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
+	@echo
+	@echo "Link check complete; look for any errors in the above output " \
+	      "or in $(BUILDDIR)/linkcheck/output.txt."
+
+doctest:
+	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
+	@echo "Testing of doctests in the sources finished, look at the " \
+	      "results in $(BUILDDIR)/doctest/output.txt."
+
+xml:
+	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
+	@echo
+	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
+
+pseudoxml:
+	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
+	@echo
+	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."

Tutorial/Source/GEPPETTO.md

@@ -0,0 +1,12 @@
+Some commands to help execute this model on GEPPETTO (under OpenSourceBrain)
+
+**Still being tested!**
+
+HHCellNetwork.electrical.getSimulationTree();
+
+Project.persist();
+
+HHCellNetwork.electrical.SimulationTree.hhpop[0].v.setWatched(true);
+
+- Press Run
+

Tutorial/Source/GEPPETTO.ses.js

@@ -0,0 +1,35 @@
+/*****
+
+A file to create info widgets, plots, save variables etc. in OSB
+
+*****/
+
+G.addWidget(Widgets.POPUP);
+Popup1.setMessage("The <b>Hodgkin-Huxley model</b> is a mathematical model that describes how action potentials in neurons are initiated and propagated. It is a set of nonlinear     differential equations that approximates the electrical characteristics of excitable cells such as neurons. <br/><br/>You can run your own simulations of this model by signing up to OSB and logging in. <br/><br/>There is also <a target='_blank' href='http://hodgkin-huxley-tutorial.readthedocs.io/en/latest/'>a tutorial for the HH model</a>, which has been developed as part of the <a target='_blank' href='http://www.openworm.org/'>OpenWorm project</a>.");
+Popup1.setName("Description");
+Popup1.setPosition(1074,142)
+Popup1.setSize(391.8,454.8)
+
+
+var Plot1 = G.addWidget(Widgets.PLOT);
+Plot1.setName("Hodgkin-Huxley Spiking Neuron");
+
+Plot1.setPosition(120, 90);
+Plot1.setSize(230,465);
+Plot1.plotData(HHCellNetwork.hhpop[0].v);
+
+var Plot2 = G.addWidget(Widgets.PLOT);
+
+Plot2.setName("Gating Variables");
+Plot2.setPosition(120,350);
+Plot2.setSize(285,465)
+Plot2.plotData(HHCellNetwork.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q);
+Plot2.plotData(HHCellNetwork.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q);
+Plot2.plotData(HHCellNetwork.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q);
+
+Plot2.setLegend(HHCellNetwork.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q,"Sodium h.q");
+Plot2.setLegend(HHCellNetwork.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q,"Sodium m.q");
+Plot2.setLegend(HHCellNetwork.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q,"Potassium n.q");
+
+HHCellNetwork.hhpop[0].v.setWatched(true);
+HHCellNetwork.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q.setWatched(true);

Tutorial/Source/GEPPETTO.singleap.ses.js

@@ -0,0 +1,43 @@
+/*****
+
+A file to create info widgets, plots, save variables etc. in OSB
+
+*****/
+
+G.addWidget(Widgets.POPUP);
+Popup1.setMessage("The <b>Hodgkin-Huxley model</b> is a mathematical model that describes how action potentials in neurons are initiated and propagated. It is a set of nonlinear     differential equations that approximates the electrical characteristics of excitable cells such as neurons. <br/><br/>You can run your own simulations of this model by signing up to OSB and logging in. <br/><br/>There is also <a target='_blank' href='http://hodgkin-huxley-tutorial.readthedocs.io/en/latest/'>a tutorial for the HH model</a>, which has been developed as part of the <a target='_blank' href='http://www.openworm.org/'>OpenWorm project</a>.");
+Popup1.setName("Description");
+Popup1.setPosition(1074,142)
+Popup1.setSize(391.8,454.8)
+
+
+var Plot1 = G.addWidget(Widgets.PLOT);
+Plot1.setName("Hodgkin-Huxley Spiking Neuron");
+
+Plot1.setPosition(120, 90);
+Plot1.setSize(230,465);
+Plot1.plotData(HHCellSingleAP.hhpop[0].v);
+
+var Plot2 = G.addWidget(Widgets.PLOT);
+
+Plot2.setName("Gating Variables");
+Plot2.setPosition(120,350);
+Plot2.setSize(285,465)
+Plot2.plotData(HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q);
+Plot2.plotData(HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q);
+Plot2.plotData(HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q);
+
+Plot2.setLegend(HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q,"Sodium h.q");
+Plot2.setLegend(HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q,"Sodium m.q");
+Plot2.setLegend(HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q,"Potassium n.q");
+
+
+Instances.getInstance("HHCellSingleAP.hhpop[0].v");
+HHCellSingleAP.hhpop[0].v.setWatched(true);
+
+Instances.getInstance("HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q");
+HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q.setWatched(true);
+Instances.getInstance("HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q");
+HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q.setWatched(true);
+Instances.getInstance("HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q");
+HHCellSingleAP.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q.setWatched(true);

Tutorial/Source/GEPPETTO.vclamp.ses.js

@@ -0,0 +1,59 @@
+/*****
+
+A file to create info widgets, plots, save variables etc. in OSB
+
+*****/
+
+G.addWidget(Widgets.POPUP);
+Popup1.setMessage("The <b>Hodgkin-Huxley model</b> is a mathematical model that describes how action potentials in neurons are initiated and propagated. It is a set of nonlinear     differential equations that approximates the electrical characteristics of excitable cells such as neurons. <br/><br/>You can run your own simulations of this model by signing up to OSB and logging in. <br/><br/>There is also <a target='_blank' href='http://hodgkin-huxley-tutorial.readthedocs.io/en/latest/'>a tutorial for the HH model</a>, which has been developed as part of the <a target='_blank' href='http://www.openworm.org/'>OpenWorm project</a>.");
+Popup1.setName("Description");
+Popup1.setPosition(1074,142)
+Popup1.setSize(391.8,454.8)
+
+
+var Plot1 = G.addWidget(Widgets.PLOT);
+Plot1.setName("Hodgkin-Huxley Spiking Neuron");
+
+Plot1.setPosition(120, 90);
+Plot1.setSize(230,465);
+Plot1.plotData(HHCellVClamp.hhpop[0].v);
+
+var Plot2 = G.addWidget(Widgets.PLOT);
+
+Plot2.setName("Gating Variables");
+Plot2.setPosition(120,350);
+Plot2.setSize(285,465)
+Plot2.plotData(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q);
+Plot2.plotData(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q);
+Plot2.plotData(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q);
+
+Plot2.setLegend(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q,"Sodium h.q");
+Plot2.setLegend(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q,"Sodium m.q");
+Plot2.setLegend(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q,"Potassium n.q");
+
+var Plot3 = G.addWidget(Widgets.PLOT);
+
+Plot3.setName("Conductances");
+Plot3.setPosition(120,350);
+Plot3.setSize(285,465)
+Plot3.plotData(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.g);
+Plot3.setLegend(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.g,"Sodium g");
+Plot3.plotData(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.g);
+Plot3.setLegend(HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.g,"Potassium g");
+
+
+
+Instances.getInstance("HHCellVClamp.hhpop[0].v");
+HHCellVClamp.hhpop[0].v.setWatched(true);
+
+Instances.getInstance("HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q");
+HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.m.q.setWatched(true);
+Instances.getInstance("HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q");
+HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.h.q.setWatched(true);
+Instances.getInstance("HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q");
+HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.n.q.setWatched(true);
+
+Instances.getInstance("HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.g");
+HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.naChans.naChan.g.setWatched(true);
+Instances.getInstance("HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.g");
+HHCellVClamp.hhpop[0].bioPhys1.membraneProperties.kChans.kChan.g.setWatched(true);

Tutorial/Source/HHCellNetwork.net.nml

@@ -0,0 +1,24 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2beta3.xsd"   
+         id="HHCellNetwork">
+    
+    <include href="hhcell.cell.nml"/> <!-- Include the cell definition -->
+
+    <!-- Short small current pulse input & short larger current pulse input -->
+    <pulseGenerator id="pulseGen1" delay="100ms" duration="100ms" amplitude="0.10nA"/>
+    <pulseGenerator id="pulseGen2" delay="300ms" duration="100ms" amplitude="0.35nA"/>
+
+    <network id="HHCellNetwork">
+
+        <notes>Network with a single cell based on the Hodgkin Huxley model with 2 input currents</notes>
+    
+        <population id="hhpop" component="hhcell" size="1"/>
+        <explicitInput target="hhpop[0]" input="pulseGen1"/>
+        <explicitInput target="hhpop[0]" input="pulseGen2"/>
+    </network>
+
+</neuroml>
+

Tutorial/Source/HHCellSingleAP.net.nml

@@ -0,0 +1,36 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2beta3.xsd"   
+         id="HHCellNetwork">
+    
+    <include href="hhcell.cell.nml"/> <!-- Include the cell definition -->
+
+    <!-- Short small current pulse input & short larger current pulse input -->
+    <pulseGenerator id="pulseGen1" delay="5ms" duration="25ms" amplitude="0.05nA"/>
+
+    <network id="HHCellNetwork">
+
+        <notes>Network with a single cell based on the Hodgkin Huxley model with 1 input current</notes>
+    
+        <property tag="recommended_dt_ms" value="0.025"/>
+        <property tag="recommended_duration_ms" value="50.0"/>
+             
+        <population id="hhpop" component="hhcell" type="populationList" size="1">
+           <annotation>
+                <property tag="color" value="1 0 0"/>
+            </annotation>
+            <instance id="0">
+                <location x="0" y="0" z="0"/>
+            </instance>
+        </population>
+        
+        <inputList id="Input_0" component="pulseGen1" population="hhpop">
+            <input id="0" target="../hhpop/0/hhcell" destination="synapses"/>
+        </inputList>
+        
+    </network>
+
+</neuroml>
+

Tutorial/Source/HHCellVClamp.net.nml

@@ -0,0 +1,23 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2beta3.xsd"   
+         id="HHCellVClamp">
+    
+    <include href="hhcell.cell.nml"/> <!-- Include the cell definition -->
+    <include href="vclamp.xml"/> <!-- Include the voltage clamp definition -->
+
+    <network id="HHCellNetwork">
+
+        <notes>Network with a single cell based on the Hodgkin Huxley model with a voltage clamp</notes>
+    
+        <population id="hhpop" component="hhcell" size="1"/>
+        <explicitInput target="hhpop[0]" input="vClamp"/>
+        
+    </network>
+    
+    
+
+</neuroml>
+

Tutorial/Source/HodgkinHuxley.py

@@ -0,0 +1,401 @@
+import scipy as sp
+import numpy as np
+import pylab as plt
+from scipy.integrate import odeint
+import sys
+
+class HodgkinHuxley():
+    """Full Hodgkin-Huxley Model implemented in Python"""
+
+    """ __init__ uses optional arguments """
+    """ when no argument is passed default values are used """
+
+    def __init__(self, C_m=1, gmax_Na=120, gmax_K=36, gmax_L=0.3, E_Na=50,
+                 E_K=-77, E_L=-54.387, t_n=450, delta_t=0.01,
+                 I_inj_amplitude=0, I_inj_duration=0, I_inj_delay=0,
+                 vc_delay=10, vc_duration=30, vc_condVoltage=-65,
+                 vc_testVoltage=10, vc_returnVoltage=-65, runMode='iclamp',
+                 injected_current_plot=True, gating_plot=True, cond_scaling_plot=False,
+                 cond_dens_plot=True, driving_force_plot=False,
+                 current_plot=True, memb_pot_plot=True):
+
+        self.C_m  = C_m
+        """ membrane capacitance, in uF/cm^2 """
+
+        self.gmax_Na = gmax_Na
+        """ Sodium (Na) maximum conductances, in mS/cm^2 """
+
+        self.gmax_K  = gmax_K
+        """ Postassium (K) maximum conductances, in mS/cm^2 """
+
+        self.gmax_L  = gmax_L
+        """ Leak maximum conductances, in mS/cm^2 """
+
+        self.E_Na = E_Na
+        """ Sodium (Na) Nernst reversal potentials, in mV """
+
+        self.E_K  = E_K
+        """ Postassium (K) Nernst reversal potentials, in mV """
+
+        self.E_L  = E_L
+        """ Leak Nernst reversal potentials, in mV """
+
+        self.t    = np.arange(0, t_n, delta_t)
+        """ The time to integrate over """
+
+        """ Advanced input - injection current (single rectangular pulse only) """
+
+        self.I_inj_amplitude   = I_inj_amplitude
+        """ maximum value or amplitude of injection pulse """
+
+        self.I_inj_duration = I_inj_duration
+        """ duration or width of injection pulse """
+
+        self.I_inj_delay = I_inj_delay
+        """ start time of injection pulse """
+
+        #vclamp parameters
+        self.run_mode = runMode
+        """default is current clamp"""
+
+        self.delay = vc_delay
+        """Delay before switching from conditioningVoltage to testingVoltage, in ms"""
+
+        self.duration = vc_duration
+        """Duration to hold at testingVoltage, in ms"""
+
+        self.conditioningVoltage = vc_condVoltage
+        """Target voltage before time delay, in mV"""
+
+        self.testingVoltage = vc_testVoltage
+        """Target voltage between times delay and delay + duration, in mV"""
+
+        self.returnVoltage = vc_returnVoltage
+        """Target voltage after time duration, in mV"""
+
+        self.simpleSeriesResistance = 1e7
+        """Current will be calculated by the difference in voltage between the target and parent, divided by this value, in mOhm"""
+
+        # plotting conditionals
+        self.injected_current_plot = injected_current_plot
+        self.gating_plot = gating_plot
+        self.cond_scaling_plot = cond_scaling_plot
+        self.cond_dens_plot = cond_dens_plot
+        self.driving_force_plot = driving_force_plot
+        self.current_plot = current_plot
+        self.memb_pot_plot = memb_pot_plot
+
+        self.num_plots = (int(self.injected_current_plot) +
+                          int(self.gating_plot)+ int(self.cond_scaling_plot) +
+                          int(self.cond_dens_plot) + int(self.driving_force_plot) +
+                          int(self.current_plot) + int(self.memb_pot_plot))
+
+        self.plot_count = 0
+
+    def alpha_m(self, V):
+        """Channel gating kinetics. Functions of membrane voltage"""
+        return 0.1*(V+40.0)/(1.0 - np.exp(-(V+40.0) / 10.0))
+
+    def beta_m(self, V):
+        """Channel gating kinetics. Functions of membrane voltage"""
+        return 4.0*np.exp(-(V+65.0) / 18.0)
+
+    def alpha_h(self, V):
+        """Channel gating kinetics. Functions of membrane voltage"""
+        return 0.07*np.exp(-(V+65.0) / 20.0)
+
+    def beta_h(self, V):
+        """Channel gating kinetics. Functions of membrane voltage"""
+        return 1.0/(1.0 + np.exp(-(V+35.0) / 10.0))
+
+    def alpha_n(self, V):
+        """Channel gating kinetics. Functions of membrane voltage"""
+        return 0.01*(V+55.0)/(1.0 - np.exp(-(V+55.0) / 10.0))
+
+    def beta_n(self, V):
+        """Channel gating kinetics. Functions of membrane voltage"""
+        return 0.125*np.exp(-(V+65) / 80.0)
+
+    def g_Na(self, m, h):
+        """
+        Conductance density (in mS/cm^2)
+        Sodium (Na = element name)
+
+        |  :param m:
+        |  :param h:
+        |  :return:
+        """
+        return self.gmax_Na * m**3 * h
+
+    def I_Na(self, V, m, h):
+        """
+        Membrane current (in uA/cm^2)
+        Sodium (Na = element name)
+
+        |  :param V:
+        |  :param m:
+        |  :param h:
+        |  :return:
+        """
+        return self.g_Na(m, h) * (V - self.E_Na)
+
+
+    def g_K(self, n):
+        """
+        Conductance density (in mS/cm^2)
+        Potassium (K = element name)
+
+        |  :param n:
+        |  :return:
+        """
+        return self.gmax_K  * n**4
+
+    def I_K(self, V, n):
+        """
+        Membrane current (in uA/cm^2)
+        Potassium (K = element name)
+
+        |  :param V:
+        |  :param n:
+        |  :return:
+        """
+        return self.g_K(n) * (V - self.E_K)
+
+    #  Leak
+    def I_L(self, V):
+        """
+        Membrane current (in uA/cm^2)
+        Leak
+
+        |  :param V:
+        |  :param h:
+        |  :return:
+        """
+        return self.gmax_L * (V - self.E_L)
+
+    def I_inj(self, t):
+        """
+        External Current
+
+        |  :param t: time
+        |  :return: step up to 10 uA/cm^2 at t>100
+        |           step down to 0 uA/cm^2 at t>200
+        |           step up to 35 uA/cm^2 at t>300
+        |           step down to 0 uA/cm^2 at t>400
+        """
+
+        """ running standalone python script """
+        if __name__ == '__main__':
+            return 10*(t>100) - 10*(t>200) + 35*(t>300) - 35*(t>400)
+
+        #""" running jupyterLab notebook """
+        else:
+            return self.I_inj_amplitude*(t>self.I_inj_delay) - self.I_inj_amplitude*(t>self.I_inj_delay+self.I_inj_duration)
+
+    def I_inj_vclamp(self,t,v):
+        """
+        External Current (vclamp)
+
+        |  :param t: time
+        |  :return: injector current for voltage clamp
+        |
+        """
+        if   t > (self.delay + self.duration):
+            current_A = (self.returnVoltage - v) / self.simpleSeriesResistance
+        elif t >= self.delay:
+            current_A = (self.testingVoltage - v) / self.simpleSeriesResistance
+        elif t < self.delay:
+            current_A = (self.conditioningVoltage - v) / self.simpleSeriesResistance
+        else:
+            print('Problem in injection current calculation for voltage clamp...')
+            return 0
+
+        #convert current to current density (uA/cm^2)
+        current_uA = current_A*10**6        #convert ampere to micro ampere
+        surface_area = 1000*10**-8          #surface area of 1000 um^2 converted to cm^2
+        current_density = current_uA/surface_area
+
+        return current_density
+
+    @staticmethod
+    def dALLdt(X, t, self):
+        """
+        Integrate
+
+        |  :param X:
+        |  :param t:
+        |  :return: calculate membrane potential & activation variables
+        """
+        V, m, h, n = X
+        if self.is_vclamp():
+            dVdt = (self.I_inj_vclamp(t,V) - self.I_Na(V, m, h) - self.I_K(V, n) - self.I_L(V)) / self.C_m
+        else:
+            dVdt = (self.I_inj(t) - self.I_Na(V, m, h) - self.I_K(V, n) - self.I_L(V)) / self.C_m
+
+        dmdt = self.alpha_m(V)*(1.0-m) - self.beta_m(V)*m
+        dhdt = self.alpha_h(V)*(1.0-h) - self.beta_h(V)*h
+        dndt = self.alpha_n(V)*(1.0-n) - self.beta_n(V)*n
+        return dVdt, dmdt, dhdt, dndt
+
+    def is_vclamp(self):
+        return self.run_mode=='vclamp' or self.run_mode=='Voltage Clamp'
+
+    def simulate(self, init_values=[-64.99584, 0.05296, 0.59590, 0.31773]):
+        """
+        Main simulate method for the Hodgkin Huxley neuron model
+        """
+
+        # init_values are the steady state values for v,m,h,n at zero current injection
+        X = odeint(self.dALLdt, init_values, self.t, args=(self,))
+        V = X[:,0]
+        m = X[:,1]
+        h = X[:,2]
+        n = X[:,3]
+        ina = self.I_Na(V, m, h)
+        ik = self.I_K(V, n)
+        il = self.I_L(V)
+        gna = self.g_Na(m, h)
+        gk = self.g_K(n)
+
+        # Save some of the data to file
+        with open('hh_py_v.dat','w') as f:
+            for ti in range(len(self.t)):
+                f.write('%s\t%s\n'%(self.t[ti],V[ti]))
+
+        if not '-nogui' in sys.argv:
+            #increase figure and font size for display in jupyter notebook
+
+
+            if __name__ != '__main__':
+                plt.rcParams['figure.figsize'] = [7, 7]
+                #plt.rcParams['font.size'] = 15
+                #plt.rcParams['legend.fontsize'] = 12
+                plt.rcParams['legend.loc'] = "upper right"
+                #
+            else:
+                plt.rcParams['figure.figsize'] = [10, 7]
+
+            plt.close()
+
+            fig=plt.figure(figsize=(7, self.num_plots * 2))
+            fig.canvas.header_visible = False
+            # plt.xlim([np.min(self.t),np.max(self.t)])  #for all subplots
+
+            if self.injected_current_plot:
+                ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                plt.title('Simulation of Hodgkin Huxley model neuron')
+                if self.is_vclamp():
+                    i_inj_values = [self.I_inj_vclamp(t,v) for t,v in zip(self.t,V)]
+                else:
+                    i_inj_values = [self.I_inj(t) for t in self.t]
+
+                if self.is_vclamp(): plt.ylim(-2000,3000)
+
+                plt.plot(self.t, i_inj_values, 'k')
+                plt.ylabel('$I_{inj}$ ($\\mu{A}/cm^2$)')
+
+                self.plot_count += 1
+
+
+            if self.gating_plot:
+                try:
+                    plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                plt.plot(self.t, m, 'r', label='$m$')
+                plt.plot(self.t, h, 'g', label='$h$')
+                plt.plot(self.t, n, 'b', label='$n$')
+                plt.ylabel('Gating variable')
+                plt.legend()
+                self.plot_count += 1
+
+            if self.cond_scaling_plot:
+                try:
+                    plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                scale_na = m*m*m*h
+                scale_k = n*n*n*n
+                plt.plot(self.t, scale_na, 'c', label='$m^{3}h$')
+                plt.plot(self.t, scale_k, 'y', label='$n^{4}$')
+                plt.ylabel('Cond scaling')
+                plt.legend()
+                self.plot_count += 1
+
+            if self.cond_dens_plot:
+                try:
+                    plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                plt.plot(self.t, gna, 'c', label='$g_{Na}$')
+                plt.plot(self.t, gk, 'y', label='$g_{K}$')
+                plt.ylabel('Cond dens ($mS/cm^2$)')
+                plt.legend()
+                self.plot_count += 1
+
+
+            if self.driving_force_plot:
+                try:
+                    ax_here = plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                    ax_here = ax1
+
+                dna = V - self.E_Na
+                dk = V - self.E_K
+                zero = [0 for v in V]
+
+                #plt.plot(self.t, dna, 'c', label='$V - E_{Na}$')
+                ax_here.fill_between(self.t, dna, color='c', alpha=0.5)
+                ax_here.fill_between(self.t, dk, color='y', alpha=0.5)
+
+                plt.plot(self.t, dna, 'c', label='$V_{m} - E_{Na}$', linewidth=0.8)
+                plt.plot(self.t, dk, 'y', label='$V_{m} - E_{K}$', linewidth=0.8)
+                plt.plot(self.t, zero, 'k', linestyle='dashed', linewidth=0.5)
+                plt.ylabel('Driving force (mV)')
+                plt.legend()
+                #if not self.is_vclamp(): plt.ylim(-85,60)
+                #plt.ylim(-1, 40)
+                self.plot_count += 1
+
+            if self.current_plot:
+                try:
+                    plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                plt.plot(self.t, ina, 'c', label='$I_{Na}$')
+                plt.plot(self.t, ik, 'y', label='$I_{K}$')
+                plt.plot(self.t, il, 'm', label='$I_{L}$')
+                plt.ylabel('Curr dens ($\\mu{A}/cm^2$)')
+                plt.legend()
+                self.plot_count += 1
+
+            if self.memb_pot_plot:
+                try:
+                    plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                plt.plot(self.t, V, 'k')
+                plt.ylabel('$V_{m}$ (mV)')
+                plt.xlabel('Time (ms)')
+                if not self.is_vclamp(): plt.ylim(-85,60)
+                #plt.ylim(-1, 40)
+                self.plot_count += 1
+
+            plt.tight_layout()
+            plt.show()
+
+if __name__ == '__main__':
+
+    if '-vclamp' in sys.argv:
+        runner = HodgkinHuxley(runMode='vclamp', t_n=50, delta_t=0.0005)
+    else: #default mode
+        runner = HodgkinHuxley(runMode='iclamp', t_n=450, delta_t=0.01)
+        
+    runner.simulate()

Tutorial/Source/LEMS_HH_Simulation.xml

@@ -0,0 +1,69 @@
+<Lems>
+
+    <!-- Example with Simple Hodgkin-Huxley cell specifying segment details-->
+
+    <!-- This is a file which can be read and executed by the LEMS Interpreter.
+         It imports the LEMS definitions of the core NeuroML 2 Components,
+         imports in "pure" NeuroML 2 and contains some LEMS elements for running
+         a simulation -->
+
+
+    <Target component="sim1"/>
+
+    <Include file="Cells.xml"/>
+    <Include file="Networks.xml"/>
+    <Include file="Simulation.xml"/>
+
+
+    <Include file="HHCellNetwork.net.nml"/>
+    <Include file="hhcell.cell.nml"/>
+    <Include file="passiveChan.channel.nml"/>
+    <Include file="naChan.channel.nml"/>
+    <Include file="kChan.channel.nml"/>
+
+
+    <Simulation id="sim1" length="450ms" step="0.01ms" target="HHCellNetwork">
+
+        <Display id="d1" title="Hodgkin-Huxley Neuron: V (mV)" timeScale="1ms" xmin="-20" xmax="470" ymin="-90" ymax="50">
+            <Line id="V" quantity="hhpop[0]/v" scale="1mV" color="#000000" timeScale="1ms"/>
+        </Display>
+
+        <Display id="d2" title="Hodgkin-Huxley Neuron: Gating Variables" timeScale="1ms" xmin="-20" xmax="470" ymin="-0.1" ymax="1.1">
+            <Line id="m" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/naChan/m/q" scale="1"  color="#ff0000" timeScale="1ms"/>
+            <Line id="h" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/naChan/h/q" scale="1"  color="#00dd00" timeScale="1ms"/>
+            <Line id="n" quantity="hhpop[0]/bioPhys1/membraneProperties/kChans/kChan/n/q" scale="1"  color="#0000ff" timeScale="1ms"/>
+        </Display>
+
+        <Display id="d3" title="Hodgkin-Huxley Neuron: Current" timeScale="1ms" xmin="-20" xmax="470" ymin="-10" ymax="10">
+            <Line id="I_na" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/iDensity" scale="1"  color="#00ffff" timeScale="1ms"/>
+            <Line id="I_k" quantity="hhpop[0]/bioPhys1/membraneProperties/kChans/iDensity" scale="1"  color="#ffff00" timeScale="1ms"/>
+            <Line id="I_l" quantity="hhpop[0]/bioPhys1/membraneProperties/leak/iDensity" scale="1"  color="#ff00ff" timeScale="1ms"/>
+        </Display>
+
+        <Display id="d4" title="Hodgkin-Huxley Neuron: I_inj (nA)" timeScale="1ms" xmin="-20" xmax="470" ymin="-0.01" ymax="0.4">
+            <Line id="I_inj1" quantity="hhpop[0]/pulseGen1/i" scale="1nA"  color="#ffffff" timeScale="1ms"/>
+            <Line id="I_inj2" quantity="hhpop[0]/pulseGen2/i" scale="1nA"  color="#000000" timeScale="1ms"/>
+        </Display>
+
+        <!-- Saved the membrane potential to file: hh_v.dat -->
+        <OutputFile id="of0" fileName="hh_v.dat">
+            <OutputColumn id="v" quantity="hhpop[0]/v"/>
+        </OutputFile>
+
+        <!-- for plotting results in JupyterNotebook -->
+        <OutputFile id="of1" fileName="hh_forJupyterNotebook.dat">
+            <OutputColumn id="v" quantity="hhpop[0]/v"/>
+            <OutputColumn id="m" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/naChan/m/q"/>
+            <OutputColumn id="h" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/naChan/h/q"/> 
+            <OutputColumn id="n" quantity="hhpop[0]/bioPhys1/membraneProperties/kChans/kChan/n/q"/>
+            <OutputColumn id="I_na" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/iDensity"/>
+            <OutputColumn id="I_k"  quantity="hhpop[0]/bioPhys1/membraneProperties/kChans/iDensity"/>
+            <OutputColumn id="I_l"  quantity="hhpop[0]/bioPhys1/membraneProperties/leak/iDensity"/>
+            <OutputColumn id="I_inj1" quantity="hhpop[0]/pulseGen1/i"/>
+            <OutputColumn id="I_inj2" quantity="hhpop[0]/pulseGen2/i"/>
+        </OutputFile>
+
+    </Simulation>
+
+
+</Lems>

Tutorial/Source/LEMS_HH_SingleAP.xml

@@ -0,0 +1,41 @@
+<Lems>
+
+    <!-- Example with Simple Hodgkin-Huxley cell specifying segment details-->
+
+    <!-- This is a file which can be read and executed by the LEMS Interpreter.
+         It imports the LEMS definitions of the core NeuroML 2 Components, 
+         imports in "pure" NeuroML 2 and contains some LEMS elements for running 
+         a simulation -->
+
+
+    <Target component="sim1"/>
+
+    <Include file="Cells.xml"/>
+    <Include file="Networks.xml"/>
+    <Include file="Simulation.xml"/>
+
+
+    <Include file="HHCellSingleAP.net.nml"/>
+    
+    <Include file="hhcell.cell.nml"/>
+    <Include file="passiveChan.channel.nml"/>
+    <Include file="naChan.channel.nml"/>
+    <Include file="kChan.channel.nml"/>
+
+
+    <Simulation id="sim1" length="50ms" step="0.001ms" target="HHCellNetwork">
+    
+        <Display id="d1" title="Hodgkin-Huxley Neuron: V (mV)" timeScale="1ms" xmin="-5" xmax="55" ymin="-90" ymax="50">
+            <Line id="V" quantity="hhpop/0/hhcell/v" scale="1mV" color="#000000" timeScale="1ms"/>
+        </Display>
+
+        
+        <OutputFile id="of0" fileName="hh_v.dat">
+            <OutputColumn id="v" quantity="hhpop/0/hhcell/v"/> 
+        </OutputFile>  
+    
+        
+    </Simulation>
+
+
+</Lems>

Tutorial/Source/LEMS_HH_VClamp.xml

@@ -0,0 +1,67 @@
+<Lems>
+
+    <!-- Example with Simple Hodgkin-Huxley cell specifying segment details-->
+
+    <!-- This is a file which can be read and executed by the LEMS Interpreter.
+         It imports the LEMS definitions of the core NeuroML 2 Components, 
+         imports in "pure" NeuroML 2 and contains some LEMS elements for running 
+         a simulation -->
+
+
+    <Target component="sim1"/>
+
+    <Include file="Cells.xml"/>
+    <Include file="Networks.xml"/>
+    <Include file="Simulation.xml"/>
+    
+    <Include file="vclamp.xml"/>
+
+
+    <Include file="HHCellVClamp.net.nml"/>
+    <Include file="hhcell.cell.nml"/>
+    <Include file="passiveChan.channel.nml"/>
+    <Include file="naChan.channel.nml"/>
+    <Include file="kChan.channel.nml"/>
+
+
+    <Simulation id="sim1" length="50ms" step="0.0001ms" target="HHCellNetwork">
+    
+        <Display id="d1" title="Hodgkin-Huxley Neuron: V (mV)" timeScale="1ms" xmin="-5" xmax="55" ymin="-90" ymax="50">
+            <Line id="V" quantity="hhpop[0]/v" scale="1mV" color="#000000" timeScale="1ms"/>
+        </Display>
+
+        <Display id="d2" title="Hodgkin-Huxley Neuron: Gating Variables" timeScale="1ms" xmin="-5" xmax="55" ymin="-0.1" ymax="1.1">
+            <Line id="m" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/naChan/m/q" scale="1"  color="#ff0000" timeScale="1ms"/>
+            <Line id="h" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/naChan/h/q" scale="1"  color="#00dd00" timeScale="1ms"/>
+            <Line id="n" quantity="hhpop[0]/bioPhys1/membraneProperties/kChans/kChan/n/q" scale="1"  color="#0000ff" timeScale="1ms"/>
+        </Display>
+
+        <Display id="d3" title="Hodgkin-Huxley Neuron: Current" timeScale="1ms" xmin="-5" xmax="55" ymin="-30" ymax="20">
+            <Line id="I_na" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/iDensity" scale="1"  color="#00ffff" timeScale="1ms"/>
+            <Line id="I_k" quantity="hhpop[0]/bioPhys1/membraneProperties/kChans/iDensity" scale="1"  color="#ffff00" timeScale="1ms"/>
+            <Line id="I_l" quantity="hhpop[0]/bioPhys1/membraneProperties/leak/iDensity" scale="1"  color="#ff00ff" timeScale="1ms"/>
+        </Display>
+
+        <Display id="d4" title="Hodgkin-Huxley Neuron: I_vClamp (nA)" timeScale="1ms" xmin="-5" xmax="55" ymin="-15" ymax="25">
+            <Line id="I_vClamp" quantity="hhpop[0]/vClamp/i" scale="1nA"  color="#ffffff" timeScale="1ms"/>
+        </Display>
+        
+        <!-- Saved the membrane potential to file: hh_v.dat -->
+        <OutputFile id="of0" fileName="hh_v.dat">
+            <OutputColumn id="v" quantity="hhpop[0]/v"/> 
+        </OutputFile>  
+    
+        <OutputFile id="of1" fileName="hh_forJupyterNotebook.dat">
+            <OutputColumn id="v" quantity="hhpop[0]/v" /> 
+			<OutputColumn id="m" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/naChan/m/q" /> 
+			<OutputColumn id="h" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/naChan/h/q" /> 
+			<OutputColumn id="n" quantity="hhpop[0]/bioPhys1/membraneProperties/kChans/kChan/n/q" /> 
+			<OutputColumn id="I_na" quantity="hhpop[0]/bioPhys1/membraneProperties/naChans/iDensity" />
+            <OutputColumn id="I_k" quantity="hhpop[0]/bioPhys1/membraneProperties/kChans/iDensity" />
+            <OutputColumn id="I_l" quantity="hhpop[0]/bioPhys1/membraneProperties/leak/iDensity" />
+			<OutputColumn id="I_vClamp" quantity="hhpop[0]/vClamp/i" />
+        </OutputFile>    
+    </Simulation>
+
+
+</Lems>

Tutorial/Source/__init__.py

@@ -0,0 +1,3 @@
+__all__ = ['HodgkinHuxley']
+
+from Source.HodgkinHuxley import HodgkinHuxley

Tutorial/Source/hhcell.cell.nml

@@ -0,0 +1,51 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2.2.xsd"
+         id="hhcell">
+
+    <include href="passiveChan.channel.nml"/> <!-- Include the channel definitions -->
+    <include href="naChan.channel.nml"/>
+    <include href="kChan.channel.nml"/>
+
+    <cell id="hhcell">
+
+        <notes>Conductance based cell model NeuroML2 format: standard Hodgkin Huxley model cell with Na, K and passive conductances</notes>
+
+        <morphology id="morphology">
+            <segment id="0" name="soma">
+                <proximal x="0" y="0" z="0" diameter="17.841242"/> <!--Gives a convenient surface area of 1000.0 um^2-->
+                <distal x="0" y="0" z="0" diameter="17.841242"/>
+            </segment>
+
+            <segmentGroup id="soma_group">
+                <member segment="0"/>
+            </segmentGroup>
+
+        </morphology>
+
+        <biophysicalProperties id="bioPhys1">
+
+            <membraneProperties>
+
+                <channelDensity id="leak" ionChannel="passiveChan" condDensity="0.3 mS_per_cm2" erev="-54.387mV" ion="non_specific"/>
+                <channelDensity id="naChans" ionChannel="naChan" condDensity="120.0 mS_per_cm2" erev="50.0 mV" ion="na"/>
+                <channelDensity id="kChans" ionChannel="kChan" condDensity="36 mS_per_cm2" erev="-77mV" ion="k"/>
+
+                <spikeThresh value="-20mV"/>
+                <specificCapacitance value="1.0 uF_per_cm2"/>
+                <initMembPotential value="-65mV"/>
+
+            </membraneProperties>
+
+            <intracellularProperties>
+                <resistivity value="0.03 kohm_cm"/>   <!-- Note: not used in single compartment simulations -->
+            </intracellularProperties>
+
+        </biophysicalProperties>
+
+    </cell>
+
+
+</neuroml>

Tutorial/Source/kChan.channel.nml

@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2beta3.xsd"   
+         id="kChan">
+
+
+    <ionChannelHH id="kChan" conductance="10pS" species="k">
+        
+        <notes>Single ion channel in NeuroML2 format: standard Potassium channel from the Hodgkin Huxley model</notes>
+
+        <gateHHrates id="n" instances="4">
+            <forwardRate type="HHExpLinearRate" rate="0.1per_ms" midpoint="-55mV" scale="10mV"/>
+            <reverseRate type="HHExpRate" rate="0.125per_ms" midpoint="-65mV" scale="-80mV"/>
+        </gateHHrates>
+            
+    </ionChannelHH>
+
+
+</neuroml>
+

Tutorial/Source/naChan.channel.nml

@@ -0,0 +1,27 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2beta3.xsd"   
+         id="naChan">
+   
+
+    <ionChannelHH id="naChan" conductance="10pS" species="na">
+        
+        <notes>Single ion channel in NeuroML2 format: standard Sodium channel from the Hodgkin Huxley model</notes>
+
+        <gateHHrates id="m" instances="3">
+            <forwardRate type="HHExpLinearRate" rate="1per_ms" midpoint="-40mV" scale="10mV"/>
+            <reverseRate type="HHExpRate" rate="4per_ms" midpoint="-65mV" scale="-18mV"/>
+        </gateHHrates>
+
+        <gateHHrates id="h" instances="1">
+            <forwardRate type="HHExpRate" rate="0.07per_ms" midpoint="-65mV" scale="-20mV"/>
+            <reverseRate type="HHSigmoidRate" rate="1per_ms" midpoint="-35mV" scale="10mV"/>
+        </gateHHrates>
+
+    </ionChannelHH>
+
+
+</neuroml>
+

Tutorial/Source/nmllite/HH.py

@@ -0,0 +1,82 @@
+from neuromllite import Network, Cell, InputSource, Population, Synapse
+from neuromllite import Projection, RandomConnectivity, Input, Simulation
+import sys
+
+################################################################################
+###   Build new network
+
+net = Network(id="HHTest")
+net.notes = "Example HH cell"
+net.parameters = {"N": 1}
+
+
+cell = Cell(id="hhcell", neuroml2_source_file="../hhcell.cell.nml")
+cell.parameters = {}
+
+params = {
+}
+
+
+for p in params:
+    cell.parameters[p] = p
+    net.parameters[p] = params[p]
+
+net.cells.append(cell)
+
+
+pop = Population(
+    id="hhPop", size="1", component=cell.id, properties={"color": ".7 0 0"}
+)
+net.populations.append(pop)
+
+net.parameters["delay"] = "5ms"
+net.parameters["stim_amp"] = "0.05nA"
+net.parameters["duration"] = "25ms"
+input_source = InputSource(
+    id="iclamp_0",
+    neuroml2_input="pulseGenerator",
+    parameters={"amplitude": "stim_amp", "delay": "delay", "duration": "duration"},
+)
+net.input_sources.append(input_source)
+
+net.inputs.append(
+    Input(
+        id="stim",
+        input_source=input_source.id,
+        population=pop.id,
+        percentage=100,
+        weight=1,
+    )
+)
+
+print(net)
+print(net.to_json())
+net_yaml_file = net.to_yaml_file("%s.nmllite.yaml" % net.id)
+net_json_file = net.to_json_file("%s.nmllite.json" % net.id)
+
+
+################################################################################
+###   Build Simulation object & save as JSON
+
+record_variables = {"v": {"all": "*"}}
+
+sim = Simulation(
+    id="Sim%s" % net.id,
+    network=net_yaml_file,
+    duration="50",
+    dt="0.025",
+    record_variables=record_variables,
+)
+
+sim.to_yaml_file("%s.yaml" % sim.id)
+sim.network = net_json_file
+sim.to_json_file("%s.json" % sim.id)
+
+
+################################################################################
+###   Run in some simulators
+
+from neuromllite.NetworkGenerator import check_to_generate_or_run
+import sys
+
+check_to_generate_or_run(sys.argv, sim)

Tutorial/Source/nmllite/HHTest.gv

@@ -0,0 +1,7 @@
+digraph HHTest {
+	node [color="#b20000" fontcolor="#ffffff" shape=ellipse style=filled]
+	hhPop [label=<hhPop<br/><i>1 cell</i>>]
+	node [color="#444444" fontcolor="#444444" style=""]
+	stim [label=<stim<br/><i>1 input</i>>]
+	stim -> hhPop [arrowhead=empty]
+}

Tutorial/Source/nmllite/HHTest.mdf.json

@@ -0,0 +1,70 @@
+{
+    "HHTest": {
+        "format": "ModECI MDF v0.3",
+        "graphs": {
+            "HHTest": {
+                "notes": "Example HH cell",
+                "nodes": {
+                    "hhPop_0": {
+                        "parameters": {},
+                        "input_ports": {},
+                        "output_ports": {},
+                        "notes": "Cell: [Cell(notes=None, id='hhcell', parameters={}, neuroml2_source_file='../hhcell.cell.nml', lems_source_file=None, neuroml2_cell=None, pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is defined in None and in Lems is: Component, id: hhcell, type: None,\n   parameters: {}\n   parent: None\n"
+                    },
+                    "Input_stim_0": {
+                        "parameters": {
+                            "amplitude": {
+                                "value": 5.000000000000001e-11
+                            },
+                            "delay": {
+                                "value": 0.005
+                            },
+                            "duration": {
+                                "value": 0.025
+                            },
+                            "i": {
+                                "conditions": {
+                                    "condition_0": {
+                                        "test": "t < delay",
+                                        "value": "0"
+                                    },
+                                    "condition_1": {
+                                        "test": "t >= delay and t < duration + delay",
+                                        "value": "weight * amplitude"
+                                    },
+                                    "condition_2": {
+                                        "test": "t >= duration + delay",
+                                        "value": "0"
+                                    }
+                                }
+                            },
+                            "t": {
+                                "default_initial_value": 0,
+                                "time_derivative": "1"
+                            },
+                            "weight": {
+                                "value": 1
+                            }
+                        },
+                        "input_ports": {},
+                        "output_ports": {
+                            "i": {
+                                "value": "i"
+                            }
+                        },
+                        "notes": "Cell: [InputSource(notes=None, id='iclamp_0', parameters={'amplitude': 'stim_amp', 'delay': 'delay', 'duration': 'duration'}, neuroml2_source_file=None, neuroml2_input='pulseGenerator', lems_source_file=None, pynn_input=None)] is defined in None and in Lems is: Component, id: iclamp_0, type: pulseGenerator,\n   parameters: {'amplitude': '0.05nA', 'delay': '5ms', 'duration': '25ms'}\n   parent: None\n"
+                    }
+                },
+                "edges": {
+                    "Edge Input_stim_0 to hhPop_0": {
+                        "name": "Edge Input_stim_0 to hhPop_0",
+                        "sender_port": "i",
+                        "receiver_port": "synapses_i",
+                        "sender": "Input_stim_0",
+                        "receiver": "hhPop_0"
+                    }
+                }
+            }
+        }
+    }
+}

Tutorial/Source/nmllite/HHTest.mdf.yaml

@@ -0,0 +1,57 @@
+HHTest:
+    format: ModECI MDF v0.3
+    graphs:
+        HHTest:
+            notes: Example HH cell
+            nodes:
+                hhPop_0:
+                    parameters: {}
+                    input_ports: {}
+                    output_ports: {}
+                    notes: "Cell: [Cell(notes=None, id='hhcell', parameters={}, neuroml2_source_file='../hhcell.cell.nml',\
+                        \ lems_source_file=None, neuroml2_cell=None, pynn_cell=None,\
+                        \ arbor_cell=None, bindsnet_node=None)] is defined in None\
+                        \ and in Lems is: Component, id: hhcell, type: None,\n   parameters:\
+                        \ {}\n   parent: None\n"
+                Input_stim_0:
+                    parameters:
+                        amplitude:
+                            value: 5.000000000000001e-11
+                        delay:
+                            value: 0.005
+                        duration:
+                            value: 0.025
+                        i:
+                            conditions:
+                                condition_0:
+                                    test: t < delay
+                                    value: '0'
+                                condition_1:
+                                    test: t >= delay and t < duration + delay
+                                    value: weight * amplitude
+                                condition_2:
+                                    test: t >= duration + delay
+                                    value: '0'
+                        t:
+                            default_initial_value: 0
+                            time_derivative: '1'
+                        weight:
+                            value: 1
+                    input_ports: {}
+                    output_ports:
+                        i:
+                            value: i
+                    notes: "Cell: [InputSource(notes=None, id='iclamp_0', parameters={'amplitude':\
+                        \ 'stim_amp', 'delay': 'delay', 'duration': 'duration'}, neuroml2_source_file=None,\
+                        \ neuroml2_input='pulseGenerator', lems_source_file=None,\
+                        \ pynn_input=None)] is defined in None and in Lems is: Component,\
+                        \ id: iclamp_0, type: pulseGenerator,\n   parameters: {'amplitude':\
+                        \ '0.05nA', 'delay': '5ms', 'duration': '25ms'}\n   parent:\
+                        \ None\n"
+            edges:
+                Edge Input_stim_0 to hhPop_0:
+                    name: Edge Input_stim_0 to hhPop_0
+                    sender_port: i
+                    receiver_port: synapses_i
+                    sender: Input_stim_0
+                    receiver: hhPop_0

Tutorial/Source/nmllite/HHTest.net.nml

@@ -0,0 +1,21 @@
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"  xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2.3.xsd" id="HHTest">
+    <notes>Generated by NeuroMLlite v0.5.8
+    Generated network: HHTest
+    Generation seed: 1234
+    NeuroMLlite parameters: 
+        N = 10.0
+        delay = 5ms
+        duration = 25ms
+        stim_amp = -0.5nA</notes>
+    <include href="/Users/padraig/neuroConstruct/osb/generic/hodgkin_huxley_tutorial/Tutorial/Source/hhcell.cell.nml"/>
+    <pulseGenerator id="iclamp_0" delay="5ms" duration="25ms" amplitude="-0.5nA"/>
+    <network id="HHTest">
+        <notes>Example HH cell</notes>
+        <property tag="recommended_dt_ms" value="0.025"/>
+        <property tag="recommended_duration_ms" value="50.0"/>
+        <population id="hhPop" component="hhcell" size="1"/>
+        <inputList id="stim" population="hhPop" component="iclamp_0">
+            <input id="0" target="../hhPop[0]" destination="synapses"/>
+        </inputList>
+    </network>
+</neuroml>

Tutorial/Source/nmllite/HHTest.nmllite.json

@@ -0,0 +1,45 @@
+{
+    "HHTest": {
+        "version": "NeuroMLlite v0.5.8",
+        "notes": "Example HH cell",
+        "parameters": {
+            "N": 1,
+            "delay": "5ms",
+            "stim_amp": "0.05nA",
+            "duration": "25ms"
+        },
+        "cells": {
+            "hhcell": {
+                "parameters": {},
+                "neuroml2_source_file": "../hhcell.cell.nml"
+            }
+        },
+        "input_sources": {
+            "iclamp_0": {
+                "parameters": {
+                    "amplitude": "stim_amp",
+                    "delay": "delay",
+                    "duration": "duration"
+                },
+                "neuroml2_input": "pulseGenerator"
+            }
+        },
+        "populations": {
+            "hhPop": {
+                "size": "1",
+                "component": "hhcell",
+                "properties": {
+                    "color": ".7 0 0"
+                }
+            }
+        },
+        "inputs": {
+            "stim": {
+                "input_source": "iclamp_0",
+                "population": "hhPop",
+                "percentage": 100,
+                "weight": 1
+            }
+        }
+    }
+}

Tutorial/Source/nmllite/HHTest.nmllite.yaml

@@ -0,0 +1,31 @@
+HHTest:
+    version: NeuroMLlite v0.5.8
+    notes: Example HH cell
+    parameters:
+        N: 1
+        delay: 5ms
+        stim_amp: 0.05nA
+        duration: 25ms
+    cells:
+        hhcell:
+            parameters: {}
+            neuroml2_source_file: ../hhcell.cell.nml
+    input_sources:
+        iclamp_0:
+            parameters:
+                amplitude: stim_amp
+                delay: delay
+                duration: duration
+            neuroml2_input: pulseGenerator
+    populations:
+        hhPop:
+            size: '1'
+            component: hhcell
+            properties:
+                color: .7 0 0
+    inputs:
+        stim:
+            input_source: iclamp_0
+            population: hhPop
+            percentage: 100
+            weight: 1

Tutorial/Source/nmllite/LEMS_SimHHTest.gv

@@ -0,0 +1,254 @@
+    # GraphViz compliant export for:HHTest (Type: network)
+
+digraph SimHHTest {
+fontsize=10;
+overlap=false;
+
+
+subgraph cluster_network {
+    style=filled;
+    color="#D6eeEA";
+    node [style=filled,color=white];
+    label = "Network to be simulated";
+
+    node [shape=rectangle]; HHTest;
+    #    Population hhPop contains components of: Component(id=hhcell type=cell) 
+    node [shape=ellipse,color="white",fontcolor="black"]; hhPop;    
+    HHTest -> hhPop [len=1.00, arrowhead=diamond]
+
+}
+
+subgraph cluster_comps {
+    style=filled;
+    color="#CCFFCC";
+    node [style=filled,color=white];
+    label = "Components";
+
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>hhcell (cell)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "hhcell (cell)";
+
+    "hhPop" -> "hhcell (cell)" [label="1",len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (0)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "null (0)";
+
+    "hhcell (cell)" -> "null (0)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>morphology (morphology)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "morphology (morphology)";
+
+    "hhcell (cell)" -> "morphology (morphology)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>soma (id = 0)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "soma (id = 0)";
+
+    "morphology (morphology)" -> "soma (id = 0)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (1)</td></tr><tr><td><font color="#666666">x = 0, y = 0, z = 0, <br/>diameter = 17.841242, radius = 8.920621E-6 m, xLength = 0 m, <br/>yLength = 0 m, zLength = 0 m</font></td></tr></table>>]; "null (1)";
+
+    "soma (id = 0)" -> "null (1)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (2)</td></tr><tr><td><font color="#666666">x = 0, y = 0, z = 0, <br/>diameter = 17.841242, radius = 8.920621E-6 m, xLength = 0 m, <br/>yLength = 0 m, zLength = 0 m</font></td></tr></table>>]; "null (2)";
+
+    "soma (id = 0)" -> "null (2)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>soma_group (segmentGroup)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "soma_group (segmentGroup)";
+
+    "morphology (morphology)" -> "soma_group (segmentGroup)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (3)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "null (3)";
+
+    "soma_group (segmentGroup)" -> "null (3)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>bioPhys1 (biophysicalProperties)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "bioPhys1 (biophysicalProperties)";
+
+    "hhcell (cell)" -> "bioPhys1 (biophysicalProperties)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (4)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "null (4)";
+
+    "bioPhys1 (biophysicalProperties)" -> "null (4)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (5)</td></tr><tr><td><font color="#666666">value = -0.02 V</font></td></tr></table>>]; "null (5)";
+
+    "null (4)" -> "null (5)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (6)</td></tr><tr><td><font color="#666666">value = -0.065 V</font></td></tr></table>>]; "null (6)";
+
+    "null (4)" -> "null (6)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>leak (channelDensity)</td></tr><tr><td><font color="#666666">erev = -0.054387000000000005 V, condDensity = 3 kg^-1 m^-4 s^3 A^2</font></td></tr></table>>]; "leak (channelDensity)";
+
+    "null (4)" -> "leak (channelDensity)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>passiveChan (ionChannelPassive)</td></tr><tr><td><font color="#666666">conductance = 1.0E-11 S</font></td></tr></table>>]; "passiveChan (ionChannelPassive)";
+
+    "leak (channelDensity)" -> "passiveChan (ionChannelPassive)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (7)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "null (7)";
+
+    "passiveChan (ionChannelPassive)" -> "null (7)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>naChans (channelDensity)</td></tr><tr><td><font color="#666666">erev = 0.05 V, condDensity = 1200 kg^-1 m^-4 s^3 A^2</font></td></tr></table>>]; "naChans (channelDensity)";
+
+    "null (4)" -> "naChans (channelDensity)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>naChan (ionChannelHH)</td></tr><tr><td><font color="#666666">conductance = 1.0E-11 S</font></td></tr></table>>]; "naChan (ionChannelHH)";
+
+    "naChans (channelDensity)" -> "naChan (ionChannelHH)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (8)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "null (8)";
+
+    "naChan (ionChannelHH)" -> "null (8)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>m (gateHHrates)</td></tr><tr><td><font color="#666666">instances = 3</font></td></tr></table>>]; "m (gateHHrates)";
+
+    "naChan (ionChannelHH)" -> "m (gateHHrates)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (9)</td></tr><tr><td><font color="#666666">rate = 1000 s^-1, midpoint = -0.04 V, scale = 0.01 V</font></td></tr></table>>]; "null (9)";
+
+    "m (gateHHrates)" -> "null (9)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (10)</td></tr><tr><td><font color="#666666">rate = 4000 s^-1, midpoint = -0.065 V, scale = -0.018000000000000002 V</font></td></tr></table>>]; "null (10)";
+
+    "m (gateHHrates)" -> "null (10)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>h (gateHHrates)</td></tr><tr><td><font color="#666666">instances = 1</font></td></tr></table>>]; "h (gateHHrates)";
+
+    "naChan (ionChannelHH)" -> "h (gateHHrates)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (11)</td></tr><tr><td><font color="#666666">rate = 70 s^-1, midpoint = -0.065 V, scale = -0.02 V</font></td></tr></table>>]; "null (11)";
+
+    "h (gateHHrates)" -> "null (11)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (12)</td></tr><tr><td><font color="#666666">rate = 1000 s^-1, midpoint = -0.035 V, scale = 0.01 V</font></td></tr></table>>]; "null (12)";
+
+    "h (gateHHrates)" -> "null (12)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>kChans (channelDensity)</td></tr><tr><td><font color="#666666">erev = -0.077 V, condDensity = 360 kg^-1 m^-4 s^3 A^2</font></td></tr></table>>]; "kChans (channelDensity)";
+
+    "null (4)" -> "kChans (channelDensity)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>kChan (ionChannelHH)</td></tr><tr><td><font color="#666666">conductance = 1.0E-11 S</font></td></tr></table>>]; "kChan (ionChannelHH)";
+
+    "kChans (channelDensity)" -> "kChan (ionChannelHH)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (13)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "null (13)";
+
+    "kChan (ionChannelHH)" -> "null (13)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>n (gateHHrates)</td></tr><tr><td><font color="#666666">instances = 4</font></td></tr></table>>]; "n (gateHHrates)";
+
+    "kChan (ionChannelHH)" -> "n (gateHHrates)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (14)</td></tr><tr><td><font color="#666666">rate = 100 s^-1, midpoint = -0.055 V, scale = 0.01 V</font></td></tr></table>>]; "null (14)";
+
+    "n (gateHHrates)" -> "null (14)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (15)</td></tr><tr><td><font color="#666666">rate = 125 s^-1, midpoint = -0.065 V, scale = -0.08 V</font></td></tr></table>>]; "null (15)";
+
+    "n (gateHHrates)" -> "null (15)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (16)</td></tr><tr><td><font color="#666666">value = 0.01 kg^-1 m^-4 s^4 A^2</font></td></tr></table>>]; "null (16)";
+
+    "null (4)" -> "null (16)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (17)</td></tr><tr><td><font color="#666666"></font></td></tr></table>>]; "null (17)";
+
+    "bioPhys1 (biophysicalProperties)" -> "null (17)" [len=1.00, arrowhead=diamond]
+    node [shape=ellipse label=<<table border="0" cellborder="0"><tr><td>null (18)</td></tr><tr><td><font color="#666666">value = 0.3 kg^2 m^2 s^-3 A^-2</font></td></tr></table>>]; "null (18)";
+
+    "null (17)" -> "null (18)" [len=1.00, arrowhead=diamond]
+}
+
+subgraph cluster_compTypes {
+    style=filled;
+    color="#D6E0EA";
+    node [style="rounded, filled",color=white];
+    label = "Component Types";
+    node [style="rounded, filled",color=white];
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>cell</td></tr><tr><td><font color="#B2C0D9">neuroLexId</font></td></tr><tr><td><font color="#FF9966">State vars: v (voltage), spiking </font></td></tr><tr><td><font color="#99CC00">initMembPot = biophysicalProperties/membraneProperties/initMembPotential/value</font></td></tr><tr><td><font color="#99CC00">thresh = biophysicalProperties/membraneProperties/spikeThresh/value</font></td></tr><tr><td><font color="#99CC00">surfaceArea = SUM OF: morphology/segments[*]/surfaceArea</font></td></tr><tr><td><font color="#99CC00">totSpecCap = biophysicalProperties/totSpecCap</font></td></tr><tr><td><font color="#99CC00">totCap = totSpecCap * surfaceArea </font></td></tr><tr><td><font color="#99CC00">iChannels = biophysicalProperties/membraneProperties/totChanCurrent</font></td></tr><tr><td><font color="#99CC00">iSyn = SUM OF: synapses[*]/i</font></td></tr><tr><td><font color="#99CC00">iCa = biophysicalProperties/membraneProperties/iCa</font></td></tr><tr><td><font color="#99CC00">caConc = biophysicalProperties/intracellularProperties/caConc</font></td></tr><tr><td><font color="#99CC00">caConcExt = biophysicalProperties/intracellularProperties/caConcExt</font></td></tr><tr><td><font color="#666633">v' = (iChannels + iSyn) / totCap</font></td></tr><tr><td><font color="#996633">IF v .gt. thresh AND spiking .lt. 0.5 THEN </font></td></tr><tr><td><font color="#996633">(spiking = 1)  <br/>AND (EVENT: spike)</font></td></tr><tr><td><font color="#996633">IF v .lt. thresh THEN </font></td></tr><tr><td><font color="#996633">(spiking = 0)</font></td></tr><tr><td><font color="#666666">Exposures: spiking, iChannels (current), iSyn (current), <br/>totSpecCap (specificCapacitance), surfaceArea (area), iCa (current), <br/>caConc (concentration), caConcExt (concentration), v (voltage) </font></td></tr></table>>]; cell;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseCellMembPot</td></tr><tr><td><font color="#666666">Exposures: v (voltage) </font></td></tr></table>>]; baseCellMembPot;
+    cell -> baseCellMembPot [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseSpikingCell</td></tr></table>>]; baseSpikingCell;
+    baseCellMembPot -> baseSpikingCell [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseCell</td></tr></table>>]; baseCell;
+    baseSpikingCell -> baseCell [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseStandalone</td></tr></table>>]; baseStandalone;
+    baseCell -> baseStandalone [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>notes</td></tr></table>>]; notes;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>morphology</td></tr></table>>]; morphology;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>segment</td></tr><tr><td><font color="#662211">Consts: LEN = 1 m </font></td></tr><tr><td><font color="#B2C0D9">name</font></td></tr><tr><td><font color="#99CC00">radDist = distal/radius</font></td></tr><tr><td><font color="#99CC00">dx = distal/xLength</font></td></tr><tr><td><font color="#99CC00">dy = distal/yLength</font></td></tr><tr><td><font color="#99CC00">dz = distal/zLength</font></td></tr><tr><td><font color="#99CC00">px = proximal/xLength</font></td></tr><tr><td><font color="#99CC00">py = proximal/yLength</font></td></tr><tr><td><font color="#99CC00">pz = proximal/zLength</font></td></tr><tr><td><font color="#99CC00">length = sqrt(((dx - px) * (dx - px) + (dy - py) * (dy - py) + (dz - pz) * (dz - pz))/(LEN * LEN)) * LEN</font></td></tr><tr><td><font color="#666666">Exposures: surfaceArea (area), radDist (length), length (length) </font></td></tr></table>>]; segment;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>proximal</td></tr><tr><td><font color="#662211">Consts: MICRON = 1.0E-6 m </font></td></tr></table>>]; proximal;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>point3DWithDiam</td></tr><tr><td><font color="#669999">Params: x, y, z, <br/>diameter </font></td></tr><tr><td><font color="#662211">Consts: MICRON = 1.0E-6 m </font></td></tr></table>>]; point3DWithDiam;
+    proximal -> point3DWithDiam [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>distal</td></tr><tr><td><font color="#662211">Consts: MICRON = 1.0E-6 m </font></td></tr></table>>]; distal;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>point3DWithDiam</td></tr><tr><td><font color="#669999">Params: x, y, z, <br/>diameter </font></td></tr><tr><td><font color="#662211">Consts: MICRON = 1.0E-6 m </font></td></tr></table>>]; point3DWithDiam;
+    distal -> point3DWithDiam [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>segmentGroup</td></tr><tr><td><font color="#B2C0D9">neuroLexId</font></td></tr></table>>]; segmentGroup;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>member</td></tr><tr><td><font color="#B2C0D9">segment</font></td></tr></table>>]; member;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>biophysicalProperties</td></tr><tr><td><font color="#99CC00">totSpecCap = membraneProperties/totSpecCap</font></td></tr><tr><td><font color="#666666">Exposures: totSpecCap (specificCapacitance) </font></td></tr></table>>]; biophysicalProperties;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>membraneProperties</td></tr><tr><td><font color="#666699">REQUIRES: surfaceArea (area)</font></td></tr><tr><td><font color="#99CC00">totSpecCap = SUM OF: specificCapacitances[*]/specCap</font></td></tr><tr><td><font color="#99CC00">totChanPopCurrent = SUM OF: populations[*]/i</font></td></tr><tr><td><font color="#99CC00">totChanDensCurrentDensity = SUM OF: channelDensities[*]/iDensity</font></td></tr><tr><td><font color="#99CC00">totChanCurrent = totChanPopCurrent + (totChanDensCurrentDensity * surfaceArea)</font></td></tr><tr><td><font color="#99CC00">totChanPopCurrentCa = SUM OF: populations[ion='ca']/i</font></td></tr><tr><td><font color="#99CC00">totChanDensCurrentDensityCa = SUM OF: channelDensities[ion='ca']/iDensity</font></td></tr><tr><td><font color="#99CC00">iCa = totChanPopCurrentCa + (totChanDensCurrentDensityCa * surfaceArea)</font></td></tr><tr><td><font color="#666666">Exposures: totChanCurrent (current), iCa (current), totSpecCap (specificCapacitance) </font></td></tr></table>>]; membraneProperties;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>spikeThresh</td></tr><tr><td><font color="#669999">Params: value (voltage) </font></td></tr></table>>]; spikeThresh;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>initMembPotential</td></tr><tr><td><font color="#669999">Params: value (voltage) </font></td></tr></table>>]; initMembPotential;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>channelDensity</td></tr><tr><td><font color="#669999">Params: erev (voltage) </font></td></tr><tr><td><font color="#662211">Consts: vShift = 0 V </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">segmentGroup</font></td></tr><tr><td><font color="#B2C0D9">ion</font></td></tr><tr><td><font color="#99CC00">channelf = ionChannel/fopen</font></td></tr><tr><td><font color="#99CC00">gDensity = condDensity * channelf</font></td></tr><tr><td><font color="#99CC00">iDensity = gDensity * (erev - v)</font></td></tr><tr><td><font color="#666666">Exposures: gDensity (conductanceDensity), iDensity (currentDensity) </font></td></tr></table>>]; channelDensity;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseChannelDensityCond</td></tr><tr><td><font color="#669999">Params: condDensity (conductanceDensity) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: gDensity (conductanceDensity), iDensity (currentDensity) </font></td></tr></table>>]; baseChannelDensityCond;
+    channelDensity -> baseChannelDensityCond [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseChannelDensity</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: iDensity (currentDensity) </font></td></tr></table>>]; baseChannelDensity;
+    baseChannelDensityCond -> baseChannelDensity [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>ionChannelPassive</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">species</font></td></tr><tr><td><font color="#99CC00">fopen = 1</font></td></tr><tr><td><font color="#99CC00">g = conductance</font></td></tr><tr><td><font color="#666666">Exposures: g (conductance), fopen </font></td></tr></table>>]; ionChannelPassive;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>ionChannel</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#99CC00">conductanceScale = PRODUCT OF: conductanceScaling[*]/factor</font></td></tr><tr><td><font color="#99CC00">fopen0 = PRODUCT OF: gates[*]/fcond</font></td></tr><tr><td><font color="#99CC00">fopen = conductanceScale * fopen0</font></td></tr><tr><td><font color="#99CC00">g = conductance * fopen</font></td></tr><tr><td><font color="#666666">Exposures: g (conductance), fopen </font></td></tr></table>>]; ionChannel;
+    ionChannelPassive -> ionChannel [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>ionChannelHH</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">species</font></td></tr><tr><td><font color="#99CC00">conductanceScale = PRODUCT OF: conductanceScaling[*]/factor</font></td></tr><tr><td><font color="#99CC00">fopen0 = PRODUCT OF: gates[*]/fcond</font></td></tr><tr><td><font color="#99CC00">fopen = conductanceScale * fopen0</font></td></tr><tr><td><font color="#99CC00">g = conductance * fopen</font></td></tr><tr><td><font color="#666666">Exposures: g (conductance), fopen </font></td></tr></table>>]; ionChannelHH;
+    ionChannel -> ionChannelHH [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseIonChannel</td></tr><tr><td><font color="#669999">Params: conductance (conductance) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">neuroLexId</font></td></tr><tr><td><font color="#666666">Exposures: g (conductance), fopen </font></td></tr></table>>]; baseIonChannel;
+    ionChannelHH -> baseIonChannel [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>notes</td></tr></table>>]; notes;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>channelDensity</td></tr><tr><td><font color="#669999">Params: erev (voltage) </font></td></tr><tr><td><font color="#662211">Consts: vShift = 0 V </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">segmentGroup</font></td></tr><tr><td><font color="#B2C0D9">ion</font></td></tr><tr><td><font color="#99CC00">channelf = ionChannel/fopen</font></td></tr><tr><td><font color="#99CC00">gDensity = condDensity * channelf</font></td></tr><tr><td><font color="#99CC00">iDensity = gDensity * (erev - v)</font></td></tr><tr><td><font color="#666666">Exposures: gDensity (conductanceDensity), iDensity (currentDensity) </font></td></tr></table>>]; channelDensity;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseChannelDensityCond</td></tr><tr><td><font color="#669999">Params: condDensity (conductanceDensity) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: gDensity (conductanceDensity), iDensity (currentDensity) </font></td></tr></table>>]; baseChannelDensityCond;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseChannelDensity</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: iDensity (currentDensity) </font></td></tr></table>>]; baseChannelDensity;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>ionChannelHH</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">species</font></td></tr><tr><td><font color="#99CC00">conductanceScale = PRODUCT OF: conductanceScaling[*]/factor</font></td></tr><tr><td><font color="#99CC00">fopen0 = PRODUCT OF: gates[*]/fcond</font></td></tr><tr><td><font color="#99CC00">fopen = conductanceScale * fopen0</font></td></tr><tr><td><font color="#99CC00">g = conductance * fopen</font></td></tr><tr><td><font color="#666666">Exposures: g (conductance), fopen </font></td></tr></table>>]; ionChannelHH;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseIonChannel</td></tr><tr><td><font color="#669999">Params: conductance (conductance) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">neuroLexId</font></td></tr><tr><td><font color="#666666">Exposures: g (conductance), fopen </font></td></tr></table>>]; baseIonChannel;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>notes</td></tr></table>>]; notes;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>gateHHrates</td></tr><tr><td><font color="#FF9966">State vars: q </font></td></tr><tr><td><font color="#99CC00">rateScale = PRODUCT OF: q10Settings[*]/q10</font></td></tr><tr><td><font color="#99CC00">alpha = forwardRate/r</font></td></tr><tr><td><font color="#99CC00">beta = reverseRate/r</font></td></tr><tr><td><font color="#99CC00">fcond = q^instances</font></td></tr><tr><td><font color="#99CC00">inf = alpha/(alpha+beta)</font></td></tr><tr><td><font color="#99CC00">tau = 1/((alpha+beta) * rateScale)</font></td></tr><tr><td><font color="#666633">q' = (inf - q) / tau</font></td></tr><tr><td><font color="#666666">Exposures: alpha (per_time), beta (per_time), tau (time), <br/>inf, rateScale, fcond, <br/>q </font></td></tr></table>>]; gateHHrates;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>gate</td></tr><tr><td><font color="#666666">Exposures: fcond, q </font></td></tr></table>>]; gate;
+    gateHHrates -> gate [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseGate</td></tr><tr><td><font color="#669999">Params: instances </font></td></tr><tr><td><font color="#666666">Exposures: fcond, q </font></td></tr></table>>]; baseGate;
+    gate -> baseGate [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>HHExpLinearRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#99CC00">x = (v - midpoint) / scale</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; HHExpLinearRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseHHRate</td></tr><tr><td><font color="#669999">Params: rate (per_time), midpoint (voltage), scale (voltage) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseHHRate;
+    HHExpLinearRate -> baseHHRate [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseVoltageDepRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseVoltageDepRate;
+    baseHHRate -> baseVoltageDepRate [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>HHExpRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#99CC00">r = rate * exp((v - midpoint)/scale)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; HHExpRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseHHRate</td></tr><tr><td><font color="#669999">Params: rate (per_time), midpoint (voltage), scale (voltage) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseHHRate;
+    HHExpRate -> baseHHRate [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseVoltageDepRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseVoltageDepRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>gateHHrates</td></tr><tr><td><font color="#FF9966">State vars: q </font></td></tr><tr><td><font color="#99CC00">rateScale = PRODUCT OF: q10Settings[*]/q10</font></td></tr><tr><td><font color="#99CC00">alpha = forwardRate/r</font></td></tr><tr><td><font color="#99CC00">beta = reverseRate/r</font></td></tr><tr><td><font color="#99CC00">fcond = q^instances</font></td></tr><tr><td><font color="#99CC00">inf = alpha/(alpha+beta)</font></td></tr><tr><td><font color="#99CC00">tau = 1/((alpha+beta) * rateScale)</font></td></tr><tr><td><font color="#666633">q' = (inf - q) / tau</font></td></tr><tr><td><font color="#666666">Exposures: alpha (per_time), beta (per_time), tau (time), <br/>inf, rateScale, fcond, <br/>q </font></td></tr></table>>]; gateHHrates;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>gate</td></tr><tr><td><font color="#666666">Exposures: fcond, q </font></td></tr></table>>]; gate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseGate</td></tr><tr><td><font color="#669999">Params: instances </font></td></tr><tr><td><font color="#666666">Exposures: fcond, q </font></td></tr></table>>]; baseGate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>HHExpRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#99CC00">r = rate * exp((v - midpoint)/scale)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; HHExpRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseHHRate</td></tr><tr><td><font color="#669999">Params: rate (per_time), midpoint (voltage), scale (voltage) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseHHRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseVoltageDepRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseVoltageDepRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>HHSigmoidRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#99CC00">r = rate / (1 + exp(0 - (v - midpoint)/scale))</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; HHSigmoidRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseHHRate</td></tr><tr><td><font color="#669999">Params: rate (per_time), midpoint (voltage), scale (voltage) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseHHRate;
+    HHSigmoidRate -> baseHHRate [len=1.00, arrowhead=onormal]
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseVoltageDepRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseVoltageDepRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>channelDensity</td></tr><tr><td><font color="#669999">Params: erev (voltage) </font></td></tr><tr><td><font color="#662211">Consts: vShift = 0 V </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">segmentGroup</font></td></tr><tr><td><font color="#B2C0D9">ion</font></td></tr><tr><td><font color="#99CC00">channelf = ionChannel/fopen</font></td></tr><tr><td><font color="#99CC00">gDensity = condDensity * channelf</font></td></tr><tr><td><font color="#99CC00">iDensity = gDensity * (erev - v)</font></td></tr><tr><td><font color="#666666">Exposures: gDensity (conductanceDensity), iDensity (currentDensity) </font></td></tr></table>>]; channelDensity;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseChannelDensityCond</td></tr><tr><td><font color="#669999">Params: condDensity (conductanceDensity) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: gDensity (conductanceDensity), iDensity (currentDensity) </font></td></tr></table>>]; baseChannelDensityCond;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseChannelDensity</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: iDensity (currentDensity) </font></td></tr></table>>]; baseChannelDensity;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>ionChannelHH</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">species</font></td></tr><tr><td><font color="#99CC00">conductanceScale = PRODUCT OF: conductanceScaling[*]/factor</font></td></tr><tr><td><font color="#99CC00">fopen0 = PRODUCT OF: gates[*]/fcond</font></td></tr><tr><td><font color="#99CC00">fopen = conductanceScale * fopen0</font></td></tr><tr><td><font color="#99CC00">g = conductance * fopen</font></td></tr><tr><td><font color="#666666">Exposures: g (conductance), fopen </font></td></tr></table>>]; ionChannelHH;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseIonChannel</td></tr><tr><td><font color="#669999">Params: conductance (conductance) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#B2C0D9">neuroLexId</font></td></tr><tr><td><font color="#666666">Exposures: g (conductance), fopen </font></td></tr></table>>]; baseIonChannel;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>notes</td></tr></table>>]; notes;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>gateHHrates</td></tr><tr><td><font color="#FF9966">State vars: q </font></td></tr><tr><td><font color="#99CC00">rateScale = PRODUCT OF: q10Settings[*]/q10</font></td></tr><tr><td><font color="#99CC00">alpha = forwardRate/r</font></td></tr><tr><td><font color="#99CC00">beta = reverseRate/r</font></td></tr><tr><td><font color="#99CC00">fcond = q^instances</font></td></tr><tr><td><font color="#99CC00">inf = alpha/(alpha+beta)</font></td></tr><tr><td><font color="#99CC00">tau = 1/((alpha+beta) * rateScale)</font></td></tr><tr><td><font color="#666633">q' = (inf - q) / tau</font></td></tr><tr><td><font color="#666666">Exposures: alpha (per_time), beta (per_time), tau (time), <br/>inf, rateScale, fcond, <br/>q </font></td></tr></table>>]; gateHHrates;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>gate</td></tr><tr><td><font color="#666666">Exposures: fcond, q </font></td></tr></table>>]; gate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseGate</td></tr><tr><td><font color="#669999">Params: instances </font></td></tr><tr><td><font color="#666666">Exposures: fcond, q </font></td></tr></table>>]; baseGate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>HHExpLinearRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#99CC00">x = (v - midpoint) / scale</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; HHExpLinearRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseHHRate</td></tr><tr><td><font color="#669999">Params: rate (per_time), midpoint (voltage), scale (voltage) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseHHRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseVoltageDepRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseVoltageDepRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>HHExpRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#99CC00">r = rate * exp((v - midpoint)/scale)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; HHExpRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseHHRate</td></tr><tr><td><font color="#669999">Params: rate (per_time), midpoint (voltage), scale (voltage) </font></td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseHHRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>baseVoltageDepRate</td></tr><tr><td><font color="#666699">REQUIRES: v (voltage)</font></td></tr><tr><td><font color="#666666">Exposures: r (per_time) </font></td></tr></table>>]; baseVoltageDepRate;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>specificCapacitance</td></tr><tr><td><font color="#669999">Params: value (specificCapacitance) </font></td></tr><tr><td><font color="#B2C0D9">segmentGroup</font></td></tr><tr><td><font color="#99CC00">specCap = value</font></td></tr><tr><td><font color="#666666">Exposures: specCap (specificCapacitance) </font></td></tr></table>>]; specificCapacitance;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>intracellularProperties</td></tr><tr><td><font color="#99CC00">caConc = SUM OF: speciesList[ion='ca']/concentration</font></td></tr><tr><td><font color="#99CC00">caConcExt = SUM OF: speciesList[ion='ca']/extConcentration</font></td></tr><tr><td><font color="#666666">Exposures: caConc (concentration), caConcExt (concentration) </font></td></tr></table>>]; intracellularProperties;
+    node [shape=box label=<<table border="0" cellborder="0"><tr><td>resistivity</td></tr><tr><td><font color="#669999">Params: value (resistivity) </font></td></tr><tr><td><font color="#B2C0D9">segmentGroup</font></td></tr></table>>]; resistivity;
+}
+
+    "hhcell (cell)" -> cell [len=1.00]
+    "null (0)" -> notes [len=1.00]
+    "morphology (morphology)" -> morphology [len=1.00]
+    "soma (id = 0)" -> segment [len=1.00]
+    "null (1)" -> proximal [len=1.00]
+    "null (2)" -> distal [len=1.00]
+    "soma_group (segmentGroup)" -> segmentGroup [len=1.00]
+    "null (3)" -> member [len=1.00]
+    "bioPhys1 (biophysicalProperties)" -> biophysicalProperties [len=1.00]
+    "null (4)" -> membraneProperties [len=1.00]
+    "null (5)" -> spikeThresh [len=1.00]
+    "null (6)" -> initMembPotential [len=1.00]
+    "leak (channelDensity)" -> channelDensity [len=1.00]
+    "passiveChan (ionChannelPassive)" -> ionChannelPassive [len=1.00]
+    "null (7)" -> notes [len=1.00]
+    "naChans (channelDensity)" -> channelDensity [len=1.00]
+    "naChan (ionChannelHH)" -> ionChannelHH [len=1.00]
+    "null (8)" -> notes [len=1.00]
+    "m (gateHHrates)" -> gateHHrates [len=1.00]
+    "null (9)" -> HHExpLinearRate [len=1.00]
+    "null (10)" -> HHExpRate [len=1.00]
+    "h (gateHHrates)" -> gateHHrates [len=1.00]
+    "null (11)" -> HHExpRate [len=1.00]
+    "null (12)" -> HHSigmoidRate [len=1.00]
+    "kChans (channelDensity)" -> channelDensity [len=1.00]
+    "kChan (ionChannelHH)" -> ionChannelHH [len=1.00]
+    "null (13)" -> notes [len=1.00]
+    "n (gateHHrates)" -> gateHHrates [len=1.00]
+    "null (14)" -> HHExpLinearRate [len=1.00]
+    "null (15)" -> HHExpRate [len=1.00]
+    "null (16)" -> specificCapacitance [len=1.00]
+    "null (17)" -> intracellularProperties [len=1.00]
+    "null (18)" -> resistivity [len=1.00]
+}

Tutorial/Source/nmllite/LEMS_SimHHTest.xml

@@ -0,0 +1,33 @@
+<Lems>
+    
+    <!-- 
+
+        This LEMS file has been automatically generated using PyNeuroML v1.1.5 (libNeuroML v0.5.6)
+
+     -->
+    
+    <!-- Specify which component to run -->
+    <Target component="SimHHTest" reportFile="report.SimHHTest.txt"/>
+
+    <!-- Include core NeuroML2 ComponentType definitions -->
+    <Include file="Cells.xml"/>
+    <Include file="Networks.xml"/>
+    <Include file="Simulation.xml"/>
+    
+    <Include file="PyNN.xml"/>
+    <Include file="HHTest.net.nml"/>
+    <Include file="/Users/padraig/neuroConstruct/osb/generic/hodgkin_huxley_tutorial/Tutorial/Source/hhcell.cell.nml"/>
+   
+    <Simulation id="SimHHTest" length="50.0ms" step="0.025ms" target="HHTest" seed="5678">  <!-- Note seed: ensures same random numbers used every run -->
+        
+        <Display id="hhPop_0_v" title="Plots of hhPop_0_v" timeScale="1ms" xmin="-5.0" xmax="55.00000000000001" ymin="-80" ymax="40">
+            <Line id="hhPop_0__v" quantity="hhPop[0]/v" scale="1mV" color="#d54f33" timeScale="1ms"/>
+        </Display>
+        
+        <OutputFile id="hhPop_0_v_dat" fileName="hhPop_0.v.dat">
+            <OutputColumn id="hhPop_0__v" quantity="hhPop[0]/v"/> 
+        </OutputFile>
+        
+    </Simulation>
+
+</Lems>

Tutorial/Source/nmllite/SimHHTest.json

@@ -0,0 +1,13 @@
+{
+    "SimHHTest": {
+        "version": "NeuroMLlite v0.5.8",
+        "network": "HHTest.nmllite.json",
+        "duration": 50.0,
+        "dt": 0.025,
+        "record_variables": {
+            "v": {
+                "all": "*"
+            }
+        }
+    }
+}

Tutorial/Source/nmllite/SimHHTest.yaml

@@ -0,0 +1,8 @@
+SimHHTest:
+    version: NeuroMLlite v0.5.8
+    network: HHTest.nmllite.yaml
+    duration: 50.0
+    dt: 0.025
+    record_variables:
+        v:
+            all: '*'

Tutorial/Source/nmllite/hhPop_0.v.dat

@@ -0,0 +1,2001 @@
+              +0	    -0.065000005
+  +2.4999999e-05	    -0.064999901
+  +4.9999999e-05	    -0.064999804
+  +7.4999996e-05	    -0.064999707
+  +9.9999997e-05	    -0.064999618
+  +0.00012500001	    -0.064999521
+  +0.00014999999	    -0.064999431
+  +0.00017499999	    -0.064999335
+  +0.00019999999	     -0.06499923
+       +0.000225	    -0.064999141
+  +0.00025000001	    -0.064999051
+       +0.000275	    -0.064998955
+  +0.00029999999	    -0.064998865
+       +0.000325	    -0.064998776
+  +0.00034999999	    -0.064998694
+       +0.000375	    -0.064998604
+  +0.00039999999	    -0.064998515
+  +0.00042500001	    -0.064998426
+  +0.00044999999	    -0.064998329
+  +0.00047500001	    -0.064998247
+  +0.00050000002	    -0.064998165
+  +0.00052499998	     -0.06499809
+        +0.00055	    -0.064998001
+  +0.00057500001	    -0.064997919
+  +0.00059999997	    -0.064997844
+  +0.00062499999	     -0.06499777
+        +0.00065	    -0.064997688
+  +0.00067500002	    -0.064997606
+  +0.00069999998	    -0.064997524
+  +0.00072499999	    -0.064997442
+  +0.00075000001	    -0.064997368
+  +0.00077500002	    -0.064997301
+  +0.00079999998	    -0.064997226
+       +0.000825	    -0.064997151
+  +0.00085000001	    -0.064997077
+  +0.00087499997	    -0.064996995
+  +0.00089999998	    -0.064996928
+       +0.000925	    -0.064996861
+  +0.00095000002	    -0.064996794
+  +0.00097499997	    -0.064996719
+          +0.001	    -0.064996645
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+    +0.050000001	    -0.072096042

Tutorial/Source/nmllite/hhcell.cell.nml

@@ -0,0 +1,51 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2.2.xsd"
+         id="hhcell">
+
+    <include href="passiveChan.channel.nml"/> <!-- Include the channel definitions -->
+    <include href="naChan.channel.nml"/>
+    <include href="kChan.channel.nml"/>
+
+    <cell id="hhcell">
+
+        <notes>Conductance based cell model NeuroML2 format: standard Hodgkin Huxley model cell with Na, K and passive conductances</notes>
+
+        <morphology id="morphology">
+            <segment id="0" name="soma">
+                <proximal x="0" y="0" z="0" diameter="17.841242"/> <!--Gives a convenient surface area of 1000.0 um^2-->
+                <distal x="0" y="0" z="0" diameter="17.841242"/>
+            </segment>
+
+            <segmentGroup id="soma_group">
+                <member segment="0"/>
+            </segmentGroup>
+
+        </morphology>
+
+        <biophysicalProperties id="bioPhys1">
+
+            <membraneProperties>
+
+                <channelDensity id="leak" ionChannel="passiveChan" condDensity="0.3 mS_per_cm2" erev="-54.387mV" ion="non_specific"/>
+                <channelDensity id="naChans" ionChannel="naChan" condDensity="120.0 mS_per_cm2" erev="50.0 mV" ion="na"/>
+                <channelDensity id="kChans" ionChannel="kChan" condDensity="36 mS_per_cm2" erev="-77mV" ion="k"/>
+
+                <spikeThresh value="-20mV"/>
+                <specificCapacitance value="1.0 uF_per_cm2"/>
+                <initMembPotential value="-65mV"/>
+
+            </membraneProperties>
+
+            <intracellularProperties>
+                <resistivity value="0.03 kohm_cm"/>   <!-- Note: not used in single compartment simulations -->
+            </intracellularProperties>
+
+        </biophysicalProperties>
+
+    </cell>
+
+
+</neuroml>

Tutorial/Source/nmllite/kChan.channel.nml

@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2beta3.xsd"   
+         id="kChan">
+
+
+    <ionChannelHH id="kChan" conductance="10pS" species="k">
+        
+        <notes>Single ion channel in NeuroML2 format: standard Potassium channel from the Hodgkin Huxley model</notes>
+
+        <gateHHrates id="n" instances="4">
+            <forwardRate type="HHExpLinearRate" rate="0.1per_ms" midpoint="-55mV" scale="10mV"/>
+            <reverseRate type="HHExpRate" rate="0.125per_ms" midpoint="-65mV" scale="-80mV"/>
+        </gateHHrates>
+            
+    </ionChannelHH>
+
+
+</neuroml>
+

Tutorial/Source/nmllite/naChan.channel.nml

@@ -0,0 +1,27 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2beta3.xsd"   
+         id="naChan">
+   
+
+    <ionChannelHH id="naChan" conductance="10pS" species="na">
+        
+        <notes>Single ion channel in NeuroML2 format: standard Sodium channel from the Hodgkin Huxley model</notes>
+
+        <gateHHrates id="m" instances="3">
+            <forwardRate type="HHExpLinearRate" rate="1per_ms" midpoint="-40mV" scale="10mV"/>
+            <reverseRate type="HHExpRate" rate="4per_ms" midpoint="-65mV" scale="-18mV"/>
+        </gateHHrates>
+
+        <gateHHrates id="h" instances="1">
+            <forwardRate type="HHExpRate" rate="0.07per_ms" midpoint="-65mV" scale="-20mV"/>
+            <reverseRate type="HHSigmoidRate" rate="1per_ms" midpoint="-35mV" scale="10mV"/>
+        </gateHHrates>
+
+    </ionChannelHH>
+
+
+</neuroml>
+

Tutorial/Source/nmllite/passiveChan.channel.nml

@@ -0,0 +1,16 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2beta3.xsd"
+         id="passiveChan">
+
+     
+    <ionChannelHH id="passiveChan" conductance="10pS" type="ionChannelPassive">
+        
+        <notes>Single ion channel in NeuroML2 format: passive channel providing a leak conductance </notes>
+        
+    </ionChannelHH>
+
+</neuroml>
+

Tutorial/Source/nmllite/report.SimHHTest.txt

@@ -0,0 +1,8 @@
+# Report of running simulation with jLEMS v0.11.0
+Simulator=jLEMS
+SimulatorVersion=0.11.0
+SimulationFile=/Users/padraig/neuroConstruct/osb/generic/hodgkin_huxley_tutorial/Tutorial/Source/nmllite/LEMS_SimHHTest.xml
+StartTime=2023-11-03 11:50:42
+SetupTime=0.241
+RealSimulationTime=0.04
+SimulationSaveTime=0.001

Tutorial/Source/passiveChan.channel.nml

@@ -0,0 +1,16 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2  https://raw.githubusercontent.com/NeuroML/NeuroML2/master/Schemas/NeuroML2/NeuroML_v2beta3.xsd"
+         id="passiveChan">
+
+     
+    <ionChannelHH id="passiveChan" conductance="10pS" type="ionChannelPassive">
+        
+        <notes>Single ion channel in NeuroML2 format: passive channel providing a leak conductance </notes>
+        
+    </ionChannelHH>
+
+</neuroml>
+

Tutorial/Source/run.bat

@@ -0,0 +1,12 @@
+@echo off
+
+REM  #################################################
+REM  # LEMS Hodgkin Huxley Neuron Model
+REM  #
+REM  # Command to run LEMS_HH_Simulation.xml script on Windows
+REM  #
+REM  # Usage: run.bat
+REM  #
+REM  #################################################
+
+java -jar jNeuroML-0.7.2-jar-with-dependencies.jar LEMS_HH_Simulation.xml

Tutorial/Source/run.sh

@@ -0,0 +1,13 @@
+#!/bin/bash
+
+#################################################
+# LEMS Hodgkin Huxley Neuron Model
+#
+# Command to run LEMS_HH_Simulation.xml script on Linux/Mac
+#
+# Usage: ./run.sh
+#
+#################################################
+
+set -e
+java -jar jNeuroML-0.7.2-jar-with-dependencies.jar LEMS_HH_Simulation.xml

Tutorial/Source/test/.test.jnml.omt

@@ -0,0 +1,17 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+
+target: ../LEMS_HH_Simulation.xml 
+engine: jNeuroML
+mep: .test.mep
+experiments:
+  Current clamp:
+    observables:
+      spike times:
+        file: 
+          path: ../hh_v.dat
+          columns: [0,1]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+          threshold: 0
+        tolerance: 0.0030997162971523324

Tutorial/Source/test/.test.jnmlnetpyne.omt

@@ -0,0 +1,17 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+
+target: ../LEMS_HH_Simulation.xml 
+engine: jNeuroML_NetPyNE
+mep: .test.mep
+experiments:
+  Current clamp:
+    observables:
+      spike times:
+        file: 
+          path: ../hh_v.dat
+          columns: [0,1]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+          threshold: 0
+        tolerance: 0.0

Tutorial/Source/test/.test.jnmlnrn.omt

@@ -0,0 +1,17 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+
+target: ../LEMS_HH_Simulation.xml 
+engine: jNeuroML_NEURON
+mep: .test.mep
+experiments:
+  Current clamp:
+    observables:
+      spike times:
+        file: 
+          path: ../hh_v.dat
+          columns: [0,1]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+          threshold: 0
+        tolerance: 0.0

Tutorial/Source/test/.test.mep

@@ -0,0 +1,6 @@
+system: Testing a single compartment cell
+
+experiments:
+  Current clamp:
+    expected:
+      spike times: [101.94, 116.91, 131.6, 146.29, 160.97, 175.65, 190.34, 300.95, 311.36, 321.11, 330.8, 340.48, 350.15, 359.83, 369.5, 379.18, 388.86, 398.53] 

Tutorial/Source/test/.test.python.omt

@@ -0,0 +1,19 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+
+# Note: this is not really running with Brian2, it's just a way to get a general Python script tested on OMV...
+
+target: ../HodgkinHuxley.py
+engine: Brian2
+mep: .test.mep
+experiments:
+  Current clamp:
+    observables:
+      spike times:
+        file:
+          path: ../hh_py_v.dat
+          columns: [0,1]
+          scaling: [1, 1]
+        spike detection:
+          method: threshold
+          threshold: 0
+        tolerance: 0.001681202059472487

Tutorial/Source/test/.test.singleap.jnmlbrian2.omt

@@ -0,0 +1,17 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+
+target: ../LEMS_HH_SingleAP.xml 
+engine: jNeuroML_Brian2
+mep: .test.singleap.mep
+experiments:
+  Current clamp:
+    observables:
+      spike times:
+        file: 
+          path: ../hh_v.dat
+          columns: [0,1]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+          threshold: 0
+        tolerance: 0.000375281461096

Tutorial/Source/test/.test.singleap.jnmleden.omt

@@ -0,0 +1,17 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+
+target: ../LEMS_HH_SingleAP.xml
+engine: jNeuroML_EDEN
+mep: .test.singleap.mep
+experiments:
+  Current clamp:
+    observables:
+      spike times:
+        file:
+          path: ../hh_v.dat
+          columns: [0,1]
+          scaling: [1000, 1000]
+        spike detection:
+          method: threshold
+          threshold: 0
+        tolerance: 1.2509382071607622e-08

Tutorial/Source/test/.test.singleap.jnmlmoose.omt

@@ -0,0 +1,17 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+
+target: ../LEMS_HH_SingleAP.xml
+engine: jNeuroML_Moose
+mep: .test.singleap.mep
+experiments:
+  Current clamp:
+    observables:
+      spike times:
+        file:
+          path: ../hh_v.dat
+          columns: [0,1]
+          scaling: [1000, 1000]
+        spike detection:
+          method: threshold
+          threshold: 0
+        tolerance: 0.000750562922191561

Tutorial/Source/test/.test.singleap.jnmlnrn.omt

@@ -0,0 +1,17 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+
+target: ../LEMS_HH_SingleAP.xml 
+engine: jNeuroML_NEURON
+mep: .test.singleap.mep
+experiments:
+  Current clamp:
+    observables:
+      spike times:
+        file: 
+          path: ../hh_v.dat
+          columns: [0,1]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+          threshold: 0
+        tolerance: 0.0

Tutorial/Source/test/.test.singleap.jnmlpynnnrn.omt

@@ -0,0 +1,17 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+
+target: ../LEMS_HH_SingleAP.xml 
+engine: jNeuroML_PyNN_NEURON
+mep: .test.singleap.mep
+experiments:
+  Current clamp:
+    observables:
+      spike times:
+        file: 
+          path: ../hh_v.dat
+          columns: [0,1]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+          threshold: 0
+        tolerance: 0.0