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History of changes in the official releases of NeuroML 2/LEMS

Note, this records major changes/updates in not just the NeuroML Schemas and LEMS, but also the Python (libNeuroML and PyLEMS) and Java (jLEMS, org.neuroml.model, jNeuroML, etc.) libraries.

Only contributors who are not NeuroML Editors are specifically pointed out below.

v2.2 / 2021-12-15

  • Renamed the main Schema from NeuroML_v2.1.xsd to NeuroML_v2.2.xsd.

  • Significant updates to documentation in NeuroML LEMS XML definitions. Have been propagated to core documentation e.g. https://docs.neuroml.org/Userdocs/Schemas/Networks.html

  • Major changes to pyNeuroML & libNeuroML to aid usability. These updates have been reflected in the NeuroML user guides & other documentation, e.g.: https://docs.neuroml.org/Userdocs/GettingStarted.html

  • Updates to automated testing of Python & Java libraries: all moved to GitHub Actions (for overview see here); updated to test on latest version of Python & Java and Windows/Mac.

  • Removed ValueAcrossSegOrSegGroup (NeuroML#165 that fixes NeuroML#162) and update any elements using it to directly include Value and SegmentGroup attributes. This allows their correct validation.

v2.1 / 2021-03-22

v2.0 / 2020-04-06

  • Renamed the main Schema from NeuroML_v2beta5.xsd to NeuroML_v2.0.xsd. Changes outlined below are reflected in the new schema

Example NeuroML models

  • The standard NeuroML examples have been updated/revised (and tests added) to ensure as many as possible of them run when converted to NEURON via jNeuroML.

Export formats

  • Improved export to graphical view of LEMS ComponentTypes, e.g. try: jnml LEMS_NML2_Ex2_Izh.xml -graph

  • Improved/retested export to SBML & SED-ML. Added export option -sbml-sedml, which converts LEMS file to SBML format with a SED-ML file describing the experiment to run. Successfully tested with Tellurium SBML simulator.

  • Minor updates to improve export to Moose. Latest developments with Moose support outlined here: NeuroML#94

v2beta5 / 2019-11-05

  • Renamed the main Schema from NeuroML_v2beta4.xsd to NeuroML_v2beta5.xsd. Changes outlined below are reflected in the new schema

Core NeuroML elements

  • Allows weighted electrical and continuous connections (examples)

  • Allows weighted inputs (e.g. define one pulseGenerator and have individually weighted inputs from this to each cell in population; examples)

  • Adds <channelDensityVShift> which can be used to add a channel with an explicit vshift parameter (e.g. adjust activation variable s by 10mV; example)

  • Adds input type <spikeGeneratorRefPoisson>, a poisson spike generator with a refractory period (example)

  • Adds <voltageClampTriple>, a voltage clamp with 3 clamp levels (definition)

  • Adds <doubleSynapse>, a single synaptic mechanism containing multiple currents, which can be used e.g. to bundle an AMPA and NMDA on one synapse, which can in turn be used for individual connections in projections (example)

LEMS functionality

  • Add option to put reportFile="report.txt" in Target component in <Lems> to save short report of simulator version, runtime etc. (example)

Bug fixes

  • Fixes for issue with statistics of stochastic spiking inputs at very high frequencies (see issue)

Simulator support

Library support

Models

v2beta4 / 2016-6-27

  • Renamed the main Schema from NeuroML_v2beta3.xsd to NeuroML_v2beta4.xsd. Changes outlined below are reflected in the new schema

Cell types

  • Added <pinskyRinzelCA3Cell>, based on Pinsky and Rinzel 1994. See here for more. Thanks to Justas Birgolas for this.

  • Added <izhikevich2007Cell>. Version of Izhikevich cell from 2007 book has been added. Main advantage is explicit capacitance, allowing dimensional currents to be applied. See here for more.

  • Added <fitzHughNagumo1969Cell> Version of FitzHugh Nagumo cell based on 1969 model. See https://github.com/OpenSourceBrain/FitzHugh-Nagumo. Thanks to Boris Marin.

  • Cells with 2 independent pools of Ca2+. The <cell2CaPools> has been added for cells with 2 independent pools of Ca2+. This may be required where some Ca channels contribute to changes in internal [Ca2+] (thus influencing [Ca2+] dependent K channels) and some don't (just pass charge). See here for example. Thanks to Rokas Stanislovas.

Synapses

  • Gap junctions and analog synapses. Added support for gap junctions (example) and analog synapses (example). Supported in jLEMS and NEURON mapping via jNeuroML.

  • New synapse types <alphaSynapse> and <expThreeSynapse>. Added synapse types <alphaSynapse> (rise time = decay time) and <expThreeSynapse> (1 exponential rise time, 2 decay times). See here

  • Improved recording from multiple synapses on multicompartmental cells. See here for an example of recording/saving of different variables on multiple synapses on a multicompartmental cell.

Ion channels/conductances

  • Fractional conductances from sub gates in channels. Added <gateFractional> which allows multiple children <subGate>, each of which has a fractional conductance. See here and here. Thanks to Boris Marin.

  • Gate with instantaneous opening. Added <gateHHInstantaneous> with only <steadyState> child, i.e. no time dependence of opening, just a voltage dependent steady state.

  • Improved support for kinetic scheme based channels. Channels based on kinetic schemes (using <ionChannelKS> and <gateKS>) have much better support in jLEMS and NEURON via jNeuroML, see here and here.

  • Alternative GHK channel density. Added a second mechanism for specifying channel densities which lead to currents based on the Goldman Hodgkin Katz current, <channelDensityGHK2>. See here and here for examples. Thanks to András Ecker.

Network

  • Connections with weights and delays. Better support in jNeuroML and NEURON for <connectionWD>. See here.

Input/output

  • Additional spiking/current inputs. New types of inputs to apply to cells, including <poissonFiringSynapse>, <transientPoissonFiringSynapse>, <timedSynapticInput> and <compoundInput>. See here for examples. Thanks to Rokas Stanislovas.

  • Saving of spike times. Added support for specifying in LEMS simulation file that spike times should be saved (as opposed to full membrane potential trace). See here for more. Thanks to Finn Krewer.

Testing

  • OMV tests on core examples. Added tests on core LEMS examples using OMV (Open Source Brain Model Validation framework). This can be used to run the examples with jNeuroML and other simulators and ensure correct spike times, etc. See the LEMSexamples/test directory. The output from these tests on 16 different simulator configurations can be seen here. Thanks to Boris Marin.

  • OMV tests on OSB models. There are ~30 different projects on OSB using NeuroML 2 which are being tested using the OMV framework. See this page for an overview of these.

Tool support

  • libNeuroML updated. libNeuroML, the Python API for reading/writing NeuroML2 has been regenerated from the latest Schema. There is also better support for Python 3.

  • pyNeuroML is a well tested alternative to jNeuroML. pyNeuroML is a Python module which can be installed with pip install pyNeuroML and can be used to access most of the functionality of jNeuroML. Thanks to Rick Gerkin for testing/updating.

Documentation

  • Consolidated web page for documentation. Direct links to all schemas, papers, libraries, tools, examples, contact details are now available on one page: https://neuroml.org/getneuroml

v2beta3 / 2014-9-11

  • Renamed the main Schema from NeuroML_v2beta2.xsd to NeuroML_v2beta3.xsd. Changes outlined below are reflected in the new schema

  • Inhomogeneous channel densities. The elements for expressing non uniform channel densities (<inhomogeneousParam>, <variableParameter>, <inhomogeneousValue>, etc. which were based on those used in NeuroML v1.8.1) have been updated, retested and improved. See https://raw.githubusercontent.com/OpenSourceBrain/L5bPyrCellHayEtAl2011/master/neuroConstruct/generatedNeuroML2/L5PC.cell.nml for an example.

  • Export compatibility check. There is now an explicit check in jNeuroML when a NeuroML 2 model is exported to a specific format (e.g. Brian, MATLAB, XPP) to see whether the model features present are supported in the export, e.g. MATLAB does not support networks but running with jLEMS does, Brain export does not support multicompartmental neurons but NEURON does, etc. A detailed error will be thrown by jnml, e.g. jnml LEMS_NML2_Ex3_Net.xml -xpp. See here for more on current model features.

  • NEURON export via jNeuroML improved. A number of improvements to the export of NEURON code from NeuroML 2 models have been made, including: much improved support for mapping multicompartmental cells in NeuroML 2 to NEURON simulations; improved handling of channel densities with Nernst based reversal potential; handling of inhomogeneous channel densities mentioned above; better saving of data (matching jLEMS saved data files); option to automatically compile the generated mod files and/or run without NEURON GUI, e.g. jnml MyLEMS.xml -neuron -nogui -run (Linux only so far).

  • Restructured NeuroML2 repository. with folders: examples (for pure NML2 files), LEMSexamples (for LEMS files using NML2 definitions) and NeuroML2CoreTypes (for the NML2 ComponentType definitions)

  • Allows a <property> element with tag/value attributes (as in NeuroML v1.8.1) in <annotation> element

  • Updated & improved <simpleSeriesResistance> element & also works now when exported to NEURON

  • Added <gateHHratesTauInf> for gates which use alpha and beta, but also have generic expressions for tau and inf using alpha and beta in non standard ways.

  • Changed Kohm to kohm in standard unit definitions.

  • Licences have been added to each of the repositories on https://github.com/NeuroML and https://github.com/LEMS.

v2beta2 / 2014-3-5

  • Renamed the main Schema from NeuroML_v2beta1.xsd to NeuroML_v2beta2.xsd. Changes outlined below are reflected in the new schema

  • Better support for GHK & Nernst. There is better support for Goldman Hodgkin Katz currents (using the channelDensityGHK element), as well as conductances whose reversal potentials are determined by the Nernst equation channelDensityNernst. See http://www.opensourcebrain.org/projects/ghk-nernst for more details and some working examples in LEMS/NeuroML2 which can be run & compared against NEURON versions. Thanks to Boris Marin for making up these examples & checking through the implementations in NeuroML.

  • Much better support in PyLEMS for NeuroML 2/LEMS models. With this release PyLEMS is a much stabler option for simulating LEMS models in general & NeuroML 2 example models. See https://github.com/LEMS/pylems/blob/master/README.md for the latest details on PyLEMS support. PyLEMS has also been put on the Python Package Index and can be installed with: pip install PyLEMS

  • Better import & export of many formats using jNeuroML. There have been significant improvements in the export & import options for a number of formats, including export of NEURON and import of SBML. See here: https://github.com/NeuroML/jNeuroML/blob/master/src/main/java/org/neuroml/JNeuroML.java for the range of export & import formats currently supported.

  • Added baseSpikingCell Added a ComponentType definition for baseSpikingCell (which indicates a cell that emits a spike), in between baseCell (which indicates any type of cell) and baseCellMembPot/baseCellMembPotDL (which are spike emitting cells with dimensional/dimensionless membrane potential)

  • Added fixedFactorConcentrationModel There has been a new element added which specifies how internal [Ca2+] varies with incoming Ca2+ current: "A fixed factor rho is used to scale the incoming current INDEPENDENTLY OF THE SIZE OF THE COMPARTMENT to produce a concentration change." This formulation is used in many models, particularly based on some of Roger Traub's, e.g. https://github.com/OpenSourceBrain/ACnet2/blob/master/neuroConstruct/generatedNeuroML2/Ca_conc.nml

  • Ordering of channelDensity more restrictive. In this release, the order of <channelPopulation>, <channelDensity>, <channelDensityNernst>, <channelDensityGHK> in <membraneProperties> is more restrictive; they have to appear in that order. See here. This is due to the previous flexibility in the ordering in the last Schema producing some difficult to understand constructs in the generated code for libNeuroML (Python) & org.neuroml.model (Java).

  • Using log as opposed to ln in Channel.xml Since ln is preferred to log for natural log in Python, Java & most programming languages, using this by default, see: https://github.com/NeuroML/NeuroML2/commit/89e600acb8e21ff0c511606156dc3d0197ed8959. Using ln in expressions will still be supported by jLEMS/jNeuroML, but is discouraged.

  • Updated the names used in complexType definitions in the Schema for some I&F cells For example IaFTauCell -> IafTauCell (note small f); this is more consistent with the NeuroML 2 element, iafTauCell, which is unchanged. However, the classes in the generated Python & Java APIs are updated to the new form, e.g. IafTauCell iaf = new IafTauCell();

v2beta1 / 2013-09-03

  • Renamed the main Schema from NeuroML_v2beta.xsd to NeuroML_v2beta1.xsd. Changes outlined below reflected in the schema

  • Modified <segment> to ensure its id is only ever a non negative integer. This required defining a BaseWithoutId element. Requested by Subhasis Ray, who pointed out the type of the segment ids should be the same in <segment id=X> and <parent segment=X>.

  • Connections with segment ids & fraction along. Added option to specify preSegmentId, preFractionAlong, etc. on connections, e.g. <connection id="0" preCellId="../popA/0/dendCell" preSegmentId="0" preFractionAlong="0.5" postCellId="../popB/0/dendCell" postSegmentId="1" postFractionAlong="1"/> to allow detailed connectivity between multicompartmental cells. See examples/NML2_SegmentConnections.nml for valid example

  • Restructure of NMDA/STP synapses. Previous elements nmdaSynapse and stpSynapse have been replaced by blockingPlasticSynapse which has optional child elements of types basePlasticityMechanism and baseBlockMechanism. See examples/NML2_SynapseTypes.nml for examples of these. Thanks to Eugenio Piasini for overhauling these synapse types.

  • networkWithTemperature added. The method for specifying the temperature to use for child elements of a network whose behaviour varies with temperature has been altered, <networkWithTemperature ...> is used now. See here for discussion on this. Example at NeuroML2CoreTypes/LEMS_NML2_Ex17_Tissue.xml

  • Channel Density with reversal potential calculated by Nernst equation. Added support for channel densities where, instead of a fixed reversal potential, the internal and external concentrations of the ion flowing through are used to calculate reversal potential. See here for more details