NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail
[['Understanding how high level brain function arises from low level mechanisms such as ion channels , synaptic transmission , neuronal integration and complex three dimensional ( 3D ) network connectivity requires detailed computational models with biologically realistic features that are able to link different levels of description and measurement .', 'Models with detailed neuronal morphologies , Hodgkin-Huxley type voltage-gated membrane conductances , and phenomenological synaptic inputs have been used to explore the determinates of action potential firing patterns and information processing in single neurons [1]–[10] .', 'This compartmental neuronal modeling approach [11] , which arose from the pioneering work of Rall [12] , has also been used to investigate the cellular basis of network behavior in various brain regions in both health and disease .', 'This includes investigation of synchronous activity [13] , [14] , oscillations [15]–[17] , sensory representation [18] , [19] , locomotion [20] and memory [21] together with the causes of epileptiform activity [15] , [22] , [23] .', 'Unfortunately , the diverse software that has been used to construct these models together with their specialized nature has restricted the wider use of such models within neuroscience .', 'A number of dedicated software packages are available for creating and simulating neuronal and network models [24] including NEURON [25] , GENESIS [26] , MOOSE [27] , NEST [28] and PSICS ( http://www . psics . org ) .', 'While dedicated simulators aid the creation of complex models , the multitude of simulator specific programming languages restricts accessibility .', 'Moreover , reproducing a model based on the detailed description in the associated paper is often difficult .', 'This splintering of the available technology has also hindered the sharing and reuse of model components and the development of new tools for detailed computational modeling .', 'This situation contrasts with the field of systems biology [29] which has benefited from the emergence of Extensible Markup Language ( XML ) based standards for describing biochemical network interactions ( e . g . SBML [30] , CellML [31] ) and curated databases of models [32] , allowing greater interoperability and validation of model behavior across multiple simulators .', 'For this reason , model sharing together with greater accessibility and interoperability of neuronal models have been identified as key areas of focus by several recent reports on neuroinformatics [33]–[35] .', 'However , the task of developing simulator-independent standards for describing the myriad of mechanisms and anatomical structures in the brain is considerably more complex than formalizing reaction schemes in systems biology .', 'The concept of a Neural Open Markup Language ( NeuroML , http://www . neuroml . org ) for neuronal model description was first proposed by Goddard et al . [36] , who extended previous work by Gardner et al . [37] .', 'Building on the ideas in this initial work , we have designed , developed and implemented a structure for NeuroML that can describe models of neuronal systems at various scales in a simulator independent manner .', 'Models of neuronal systems can vary greatly in the amount of biological detail incorporated [6] .', 'The latest version of NeuroML ( v1 . 8 . 1 ) focuses on expressing detailed neuronal models which can include complex neuronal morphologies [38] , descriptions of voltage- and ligand-gated conductances , synaptic mechanisms and the positions of cells and synaptic connections in a 3D network structure .', 'Here we provide an overview of the structure of the language , illustrate its functionality by expressing a number of complex cell and network models in NeuroML and demonstrate the interoperability and model portability it enables by reproducing model behavior on multiple independently developed simulators .'], ['The three Level structure of NeuroML partitions model descriptions into the anatomical structure and the various physiological mechanisms that underlie the electrical behavior of neurons and networks and reflects the manner in which they are commonly implemented in neuronal simulators ( Figure 1 ) .', 'Level 1 of NeuroML allows description of the neuronal morphology ( in MorphML [38] ) and relevant background data ( metadata ) associated with the model .', 'Level 2 of NeuroML builds on this in two ways: it can be used to extend Level 1 cell descriptions to include passive and active electrical properties and it includes ChannelML , which describes voltage-gated membrane conductances together with static and plastic synaptic conductance processes .', 'Descriptions of neural networks are specified in Level 3 .', 'This Level includes NetworkML , which specifies the 3D locations of neurons , connections between populations , and external electrical inputs .', 'This modular structure , together with the use of distinct schemas ( i . e . MorphML , ChannelML and NetworkML ) is designed to enable the exchange and reuse of the individual components between a wide variety of software applications .', 'Descriptions in higher Levels of NeuroML can build on components from lower Levels ( Figure 1 , Materials and Methods ) .', 'A full description of the model elements and the files used to specify them is provided in Supporting Text S1 .', 'To achieve a high degree of biological detail , data-driven compartmental models utilize data from neuronal reconstructions , measured properties of membrane and synaptic conductances , single and multiple cell electrophysiological recordings and density and connectivity data .', 'The relationship between each of these data types and the various Levels and modular components of NeuroML is illustrated in Figure 2 .', 'Models in NeuroML format can be directly imported into applications or automatically mapped onto them using a metasimulator ( e . g . neuroConstruct [39] ) and simulation results can be used to make predictions that can be tested experimentally .', 'NeuroML is an Open Source project ( http://sourceforge . net/projects/neuroml ) and the specifications are based on XML [40] , a widely used language for exchanging structured information between computer applications , which has been used previously in other standardization initiatives e . g . SBML [30] , CellML [31] , BrainML [41] and MathML [42] .', 'Figure 3 shows an example of a ChannelML file with the set of parameters required to fully describe an instance of a voltage-gated K+ channel in the Hodgkin-Huxley formalism ( See Supporting Text S1 for a description of the current and conductance which would result from this type of channel model ) .', 'This XML document is a text file containing structured data ( Figure 3A ) , which can be parsed with freely available software libraries ( i . e . with minimal effort for application developers ) and can be easily transformed into a human-readable form ( Figure 3B , Materials and Methods ) .', 'Moreover , the properties of the specified model can be readily visualized in graphical form ( Figure 3C ) .', 'Rather than requiring the restructuring of neuronal simulators to a common internal model based on NeuroML , our approach to enhance interoperability and transparency identifies the useful elements that can be exchanged between computational neuroscience tools ( morphologies , channels , synapses , network structure etc . ) and develops the means to import and export these in a standardized format .', 'This approach allows researchers to develop new neuronal and network models using the application of their choice for maximum flexibility , and then convert these models to NeuroML format for cross simulator validation , increased accessibility and storage .', "This also means that the models are run using a simulator's own internal data structures , so there is no loss of execution performance compared to creating the models from scratch in the simulator's own script .", 'NeuroML differs from the model description approaches taken by SBML and CellML , which can describe a variety of models of dynamical systems in biology using low level concepts such as compartments , variables and reaction rates .', 'In contrast NeuroML incorporates many higher level concepts such as Hodgkin-Huxley models of ion channels , synaptic conductance waveforms , synaptic plasticity models , 3D dendritic and axonal structures and 3D network connectivity , because the neuronal models it describes cover many levels of description from ion channels to whole networks .', 'Indeed it is intended for describing models containing the established neurophysiological entities most commonly used when modeling biologically detailed neural systems .', 'While this limits the scope of biological models that can be expressed in this format , it ensures that a wide range of detailed neuronal models in use today can be specified in a dedicated language and facilitates mapping of the models to widely used simulation tools .', 'The modular nature of the NeuroML language allows modelers to use only the components relevant for their system .', 'This is enabled by using a number of XML Schema ( XSD ) files ( see Materials and Methods ) for each part of the language .', 'The structure of the elements used to specify each component of the language is depicted in Figures 4–6 .', 'In the following sections we discuss each of the 3 Levels in more detail .', 'The first Level of the NeuroML language has two main purposes: to define neuronal morphologies ( MorphML ) and metadata , which provides additional information about model components at this and subsequent levels .', 'Cells are described by lists of segment elements , with each element containing the 3D location and shape of each segment .', 'Details of the mapping between elements in MorphML and the data structures of other applications that use morphology formats such as Neurolucida , NEURON and GENESIS have previously been described [38] , and the elements permitted for a cell description at this and subsequent Levels is shown in Figure 4 ( a detailed description of each of these elements is given in Supporting Text S1 ) .', 'Manual reconstruction of complex neuronal morphologies is a difficult and time consuming task and human errors can be difficult to detect .', 'Once converted to MorphML , the morphology files can be automatically checked for discontinuities and isolated elements .', 'MorphML also allows description of other anatomical information , which may have been recorded during cell reconstruction , such as histological features , reference points , and outlines of perceived boundaries [38] .', 'NeuroML Level 1 also allows metadata , which is important for tracking the provenance of the model components and for providing background information on the model .', 'A number of elements are included to provide structured information on the original authors of the model , translators of the model to NeuroML format , publications , and references to entries in databases such as ModelDB [43] and NeuroMorpho . org [44] , as well as text based comments .', 'The concept of model stability ( the status element ) is also included to allow a record of any known limitations of the model .', 'Two types of unit system are allowed in NeuroML , SI Units and Physiological Units ( ms , mV , cm , etc . ) , and only one of these must be used consistently in relevant elements of a NeuroML file .', 'This facilitates the correct conversion of physical quantities to the unit system of each supported application .', 'The second Level of the NeuroML language describes the electrical properties of the membrane that underlie rapid signaling in the brain .', 'The two main parts of this Level are: an extension of the morphological descriptions from Level 1 that includes details of the passive electrical properties and channel densities on various parts of the cell ( Level 2 cell in Figure 4 ) ; and ChannelML , which allows descriptions of the individual conductance mechanisms ( Figure 5 ) .', 'ChannelML supports two main types of conductances: those that arise from channels distributed over the plasma membrane ( channel_type element ) , such as voltage-gated conductances or conductances gated by intracellular ions ( e . g . [Ca2+] dependent potassium conductances ) ; and conductances arising at synaptic contacts ( synapse_type ) .', 'Distributed conductances are normally specified by describing the transition rates between channel states and their voltage dependence ( Figure 3; Supporting Text S1 ) .', 'This allows specification of channel gating models with the traditional Hodgkin-Huxley formalism ( with multiple instances of identical gates; e . g . Figure 3A ) or with more detailed state-based kinetic ( Markov ) models ( of which the HH model is a special case ) .', 'A wide range of examples of voltage-gated conductances are supported by ChannelML including those underlying fast and persistent Na+ currents , delayed rectifier , A- and M-type K+ currents , H-currents and L- and T-type Ca2+ currents .', '[Ca2+] dependent BK and SK type channels can also be expressed .', 'The commonly used Q10 function for temperature dependence of transition rates can be added .', 'While the focus of NeuroML to date has been on more detailed conductance based models , ChannelML also supports a basic integrate-and-fire neuron model .', 'However , more advanced types of reduced model such as exponential integrate and fire or Izhikevich spiking neurons are not yet supported ( see Discussion for future plans for support of more abstract neuronal representations ) .', 'Both neurotransmitter gated conductances at chemical synapses and gap junction conductances at electrical synapses are supported in ChannelML ( Figure 5 ) .', 'Conductance changes at chemical synapses are defined by a time course which can have a number of forms including an exponential rise and up to three decay components .', 'These conductances include both the simple linear ohmic type ( for modeling most AMPA and GABAA receptor mediated synapses ) and non-linear voltage-dependent components ( for modeling the Mg2+ block of the NMDA receptor mediated synaptic component ) .', 'Activity dependent synaptic plasticity is implemented with two mechanisms in ChannelML: a short-term plasticity ( STP ) mechanism based on a widely used STP model [45] incorporating both depression and facilitation components and a spike timing dependent plasticity ( STDP ) mechanism based on the model of Song and Abbott [46] , but simulator support for STDP is presently limited .', 'NeuroML provides representations of phenomenological models of synaptic plasticity that can reproduce a wide range of behavior including short-term facilitation and depression and Hebbian and anti-Hebbian learning , thus accommodating synaptic plasticity over a wide range of time scales where adequate simulator support exists .', 'Level 2 also allows the location and density of membrane conductances described in ChannelML to be specified on regions of the cell ( e . g . soma , axon , apical dendrites ) .', 'The passive electrical properties of the cell ( e . g . specific axial resistance and specific membrane capacitance ) can be defined in a similar manner ( using the biophysics element; see Figure 4 ) .', 'Moreover , non-uniform channel densities can be implemented using a metric , such as the path distance from soma , and expressing the density in terms of this metric ( using the variable_parameter element ) .', 'Although NeuroML Level 2 is required for defining a full spiking neuron model , elements of the models can be defined as standalone descriptions in MorphML and ChannelML , thereby facilitating the exchange and reuse of individual model components .', 'The third Level of NeuroML allows specification of the 3D anatomical structure and synaptic connectivity of a network of neurons , together with the properties of the external input used to drive the network .', 'NeuroML Level 3 has two main purposes: to define NetworkML ( Figure 6 ) and to allow extension of Level 2 cells with specification of regions of the cell membrane ( e . g . apical dendrites ) to which specific synaptic connections are limited ( connectivity element; Figure 4 ) .', 'Thus complex networks with different types of excitatory and inhibitory neurons can be defined , including dendritic sub-region specific synaptic connections .', 'There are two possible ways to describe networks in NetworkML: an explicit list of instances of cell positions and synaptic connections ( instance based representation ) ; or as an algorithmic template for describing how instances of the network should be generated , for example to place 300 cells randomly in a certain 3D region ( template based representation ) .', 'The instance based representation is quite compact , even for large scale simulations , because a network with 10 , 000 identical neurons will only have one instance of the cell description and a list of 10 , 000 locations .', 'To date , this has proven a more useful and portable format .', 'Only a limited range of network templates is currently supported , though these are in the process of being updated for the next version of NeuroML ( see Discussion ) .', 'The instance based representation can also include information on the computational node a cell should be run on ( node_id attribute ) to facilitate execution of large scale networks on parallel computing hardware ( Supporting Text S1 ) .', 'There are three core elements for describing networks in NetworkML: population specifies the numbers of cells of a specific type , together with their locations in 3D space; projection defines the set of synaptic connections between two populations or within a single population , by identifying the precise location of the synapse on the pre- and postsynaptic neuronal morphology and specifying the type of synapse ( s ) present; and input describes an external electrical input into the network .', 'Inputs can take the form of a current pulse delivered by model electrodes or random synaptic stimulation .', 'The key goals of the NeuroML initiative are to make models and their components exchangeable , simulator independent and accessible to a wide range of researchers .', 'To this end several software applications for compartmental modeling have been extended to support NeuroML .', 'The most extensive support for NeuroML at present is provided by neuroConstruct which is an application for building , visualizing and analyzing networks of compartmental neurons in 3D space [39] .', 'It uses an internal representation for cells that is closely related to NeuroML and can import and export model components in MorphML , ChannelML and NetworkML ( in XML or a more compact HDF5-based binary format ) or a complete description of a network model in a single Level 3 file .', 'There is support for plotting channel properties ( e . g . voltage dependence of rates; Figure 3C ) and analyzing properties of neuronal morphologies and networks .', 'Simulator specific scripts for NEURON , GENESIS , MOOSE , PSICS and PyNN can also be automatically generated from NeuroML files and executed , and simulation results can be reloaded for visualization and analysis ( Figure 2 ) .', 'NEURON allows native import and export of cells in both Level 1 and 2 NeuroML formats [47] .', 'This allows cell models created in NEURON native scripts to be exported in a standardized format .', 'All channel types currently in ChannelML can be converted to NEURON due to the flexible nature of the NMODL language [48] .', 'GENESIS 2 [26] does not natively support NeuroML , but a mapping to this format is provided via neuroConstruct .', 'MOOSE ( Multiscale Object-Oriented Simulation Environment ) [27] has been developed as part of the GENESIS 3 initiative , but is based on a complete reimplementation of the core of GENESIS .', 'Scripts specifically for MOOSE can be generated by neuroConstruct and are for the most part identical to GENESIS 2 scripts , and native support for NeuroML in MOOSE is in development .', 'NeuroML mappings have also been created for the recently developed PSICS simulator , and scripts for running single cell models on this simulator can be generated through neuroConstruct .', 'There is also some native support in PSICS for importing MorphML and ChannelML .', 'PyNN [49] is a Python package for creating network models for multiple simulators ( including NEST [28] and NEURON ) , and support for mappings to and from NeuroML has recently been added .', 'Table 1 summarizes the current support in each of the aforementioned tools for various types of models which can be expressed in NeuroML .', 'In addition to the applications mentioned here , native support for various parts of NeuroML is currently in development in software applications not associated with the authors of this paper , including CX3D [50] and PCSIM [51] .', 'NeuroML support is in development for Neurospaces [52] , also being developed as part of the GENESIS 3 initiative .', 'The latest details of software support for NeuroML can be found at http://www . neuroml . org/tool_support .', 'To help researchers convert their existing models to NeuroML , we have generated a number of sample documents on the NeuroML website ( http://www . neuroml . org/examples ) , which can be viewed in the original XML or converted to more readable formats ( e . g . Figure 3B ) .', 'There is also a software application for validating NeuroML files to check that they are compliant .', 'MorphML cells and NetworkML files can be converted for visualizing in 3D in a web browser using an X3D compatible plug-in .', 'Moreover , MorphML and ChannelML files can be converted online to a number of simulator formats including NEURON , GENESIS/MOOSE and PSICS , using the XML Stylesheet ( XSL ) based mapping files which have been developed for each simulator ( Materials and Methods ) .', 'In order to test the mappings from NeuroML to these simulators and other tools , we have converted a number of existing , published models to NeuroML .', 'To test that NeuroML descriptions of cell morphology and conductances can produce similar results across supported simulators , we converted a morphologically detailed model of a CA1 pyramidal cell [2] with 6 active conductances from the original NEURON format into NeuroML and compared the model behavior on NEURON , GENESIS , MOOSE and PSICS .', 'This model was chosen because it contains three conductances that are non-uniformly distributed over the dendritic tree .', 'The behavior of the ChannelML representation of the 6 conductances was first verified using a single compartment cell ( Supporting Figure S1 ) .', 'The detailed 3D cell and its response to a brief current injection in the soma are shown in Figure 7 .', 'The time courses of the membrane potential at various points along the cell was directly compared for the four simulators ( Figure 7A ) .', 'Despite important differences in the way each simulator handles the simulation of the cell anatomy and channels ( e . g . the morphology was mapped to a reduced number of compartments on GENESIS/MOOSE , and the numbers of ion channels and their individual positions were explicitly calculated in PSICS; Materials and Methods ) , the physiologically measurable output of the cell was very similar across all simulators tested ( Figure 7B–D ) confirming the simulator-independence of the NeuroML model description on short timescales and for a realistic neuronal morphology .', 'To test the synaptic models defined in NeuroML , we compared the behavior of a number of supported models between simulators .', 'The ChannelML implementation of an electrical synapse was tested by comparing simulations run on GENESIS , MOOSE and NEURON .', 'The voltage responses in a pair of passive model neurons connected by a gap junction to a step current injected into one of the cells gave rise to identical results in these simulators ( Figure 8A ) .', 'Neurotransmission at excitatory chemical synapses is mediated predominantly by glutamate in the mammalian brain .', 'Glutamate typically activates AMPA receptors ( Figure 8B ) , which have a simple ohmic conductance and NMDA receptors , which exhibit a nonlinear voltage dependent conductance due to Mg2+ block ( Figure 8C ) .', 'In all cases the results from NEURON , GENESIS and MOOSE match for simulations derived from the ChannelML description .', 'The ChannelML implementation of a synaptic Short Term Plasticity ( STP ) model [45] was also compared using NEST and NEURON .', 'Altering the model parameters to favor short-term depression or facilitation gave identical results ( Figure 8D ) using both simulators .', 'To test the support for network representations in NeuroML , we converted the elements of the thalamocortical column network model developed by Traub et al . [15] to NeuroML , as this is one of the most advanced multi-cellular network models published to date .', 'The electrical behavior of the model arises from 22 voltage- and ligand-gated Na+ , K+ and Ca2+ conductances together with both electrical and chemical synapses , which were all converted to ChannelML and tested ( Figure 9A , Materials and Methods ) .', 'Each of the 14 cell types present was converted to NeuroML , using the Level 2 cell export function of NEURON and import function of neuroConstruct ( Supporting Figure S2 ) .', 'Supporting Tables S1 and S2 list the cell and channel types respectively .', 'The different complements of the channels and different morphologies gave rise to a variety of behaviors including regular spiking , fast spiking and bursting behavior ( Figure 9B–E ) .', 'The NeuroML implementation produced qualitatively similar spiking behavior for simulations run in NEURON , GENESIS and MOOSE in the 10 electrophysiologically distinct cells during sustained firing over hundreds of milliseconds to seconds ( Supporting Figure S3 ) .', 'However , differences in the timing of spikes was evident in some of the cells , unless the spatial and temporal discretisation of the cell was increased substantially .', 'Two observations confirmed that the main cause of divergence in spike times arose from the use of symmetrical compartments ( where axial resistance is split and numerical integration takes place at the center of the compartment ) and asymmetrical compartments ( axial resistance is located at one end of the compartment ) .', 'Firstly , the spike times of a single compartment cell with all the channel conductances included were indistinguishable on NEURON , GENESIS and MOOSE ( Figure 9A ) , confirming the ChannelML implementations allowed equivalent behavior on all 3 simulators .', 'Secondly , when the spatial discretisation of the cell models was increased , all simulators tended toward the same spike times ( Supporting Figure S4 ) , with GENESIS ( for which asymmetrical compartments had to be used , see Materials and Methods ) generally requiring a finer discretisation .', 'These results show that the way models are implemented on different simulators can have a significant impact on their behavior .', 'Moreover , true interoperability , as measured through model convergence , may only occur at the limits of spatial and temporal discretisation .', 'Once all the channel , synaptic and cellular components of the model were converted to NeuroML and tested , we used neuroConstruct [39] to build a 56 cell Layer 2/3 network that matched as closely as possible a previous larger scale model which uses these cells [16] .', 'This consisted of regular spiking and fast rhythmic bursting pyramidal cells and low threshold spiking , axo-axonic and basket type interneurons ( Figure 10A , Materials and Methods ) .', 'As specified in the original model , excitatory and inhibitory synaptic conductances were located on specific dendritic and somatic segments and electrical synapses were included within cell populations .', 'This network model was not tuned against any new experimental data and is primarily intended as a test case for comparison of network behavior across simulators .', 'The spike times of the neuronal populations were similar across the 3 simulators over the first 200 ms of the simulation , when a small simulation timestep and fine spatial discretisation was used ( Figure 10B ) .', 'At longer times , some spikes became shifted and others appeared or disappeared depending on the simulator .', 'This divergence in model behavior occurred earlier in the simulation run and was much more pronounced when a more typical time step and coarser discretisation was used ( Supporting Figure S5 ) , suggesting that in practice , the precise spike times , and even the occurrence of some spikes produced by complex network models , will depend on the simulator implementation .', 'A complete description of this network model including cell structure , channels , synapses , and lists of cell locations and connections can be represented in a single Level 3 NeuroML file .'], ['We have developed , implemented and tested NeuroML , a simulator-independent neuronal model description language for defining data-driven models of neurons and networks with a high degree of biological detail .', 'This XML based language has a modular structure and the current version is sufficiently advanced to allow the description of the complex branching structures of dendritic trees and axonal projections , their biophysical properties , voltage- and calcium-gated ion channels , chemical synapses with short-term synaptic plasticity , electrical synapses , and both large and small scale network structure .', 'The implementation and interoperability of models expressed in NeuroML were tested and the functionality illustrated by expressing existing single neuron and network models of different brain regions in this format and by demonstrating equivalent model behavior on different simulators .', 'Providing a structured , declarative framework for describing detailed neuronal models that is independent of any particular simulator implementation has a number of important benefits .', 'Firstly , the behavioral properties of a model specified in NeuroML can be compared across simulators .', 'This is important for testing the validity of results from a model , since all conclusions should be simulator-independent .', 'Model comparison also aids bug identification , tests the robustness of a particular model implementation , highlights performance bottlenecks and promotes collaboration between different simulator communities .', 'Secondly , describing model components with structured schemas written in XML facilitates machine automated validation of particular components ( e . g . the integrity of a complex neuronal morphology defined in MorphML ) .', 'Thirdly , the modular structure of NeuroML , and the standalone nature of many of the mechanisms , facilitates reuse of model components .', 'This speeds up the construction of models and allows models with increasing biological detail to be built from previously developed components .', 'This is important because detailed conductance-based neuronal models are labor intensive to develop , often taking years to go from initial experiments to published model .', 'Enabling interoperability will accelerate the rate of progress by allowing investigators to use and extend previous work , rather than ‘reinventing the wheel’ each time they want to build a new model .', 'Such models will also provide a ready-made resource for developing and testing new software tools in this area .', 'Model components defined in NeuroML can be automatically transformed into textual and graphical formats familiar to neurophysiologists ( Figure 3B , C ) .', 'This allows access to the mechanisms and parameters underlying the model for researchers unfamiliar with simulator scripting languages .', 'Moreover , NeuroML compliant tools with user friendly graphical user interfaces , such as neuroConstruct , allow neuronal and network models to be visualized , modified and run without the need to write code .', 'This increased accessibility and transparency also allows critical evaluation by a wider range of neuroscientists including both theoreticians and experimentalists .', 'Publicly exposing the details of a model implementation will discourage poor practices , improving the quality and robustness of models .', 'By providing a common language for simulators and tools to interact , NeuroML can help reduce the barriers between computational and experimental neuroscience , thereby encouraging wider use of such detailed models .', 'Translation of an existing m
['Introduction', 'Results', 'Discussion', 'Materials and Methods']
['Biologically detailed single neuron and network models are important for understanding how ion channels , synapses and anatomical connectivity underlie the complex electrical behavior of the brain .', 'While neuronal simulators such as NEURON , GENESIS , MOOSE , NEST , and PSICS facilitate the development of these data-driven neuronal models , the specialized languages they employ are generally not interoperable , limiting model accessibility and preventing reuse of model components and cross-simulator validation .', 'To overcome these problems we have used an Open Source software approach to develop NeuroML , a neuronal model description language based on XML ( Extensible Markup Language ) .', 'This enables these detailed models and their components to be defined in a standalone form , allowing them to be used across multiple simulators and archived in a standardized format .', 'Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances , models of electrical coupling , synaptic transmission and short-term plasticity , together with morphologically detailed models of individual neurons .', 'We have also used these NeuroML-based components to develop an highly detailed cortical network model .', 'NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators .', 'Although our results confirm that simulations run on different simulators converge , they reveal limits to model interoperability , by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation , when the computational overhead is high .', 'Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments , thereby improving model transparency , accessibility and reuse in computational neuroscience .']
['Computer modeling is becoming an increasingly valuable tool in the study of the complex interactions underlying the behavior of the brain .', 'Software applications have been developed which make it easier to create models of neural networks as well as detailed models which replicate the electrical activity of individual neurons .', 'The code formats used by each of these applications are generally incompatible however , making it difficult to exchange models and ideas between researchers .', 'Here we present the structure of a neuronal model description language , NeuroML .', 'This provides a way to express these complex models in a common format based on the underlying physiology , allowing them to be mapped to multiple applications .', 'We have tested this language by converting published neuronal models to NeuroML format and comparing their behavior on a number of commonly used simulators .', 'Creating a common , accessible model description format will expose more of the model details to the wider neuroscience community , thus increasing their quality and reliability , as for other Open Source software .', 'NeuroML will also allow a greater “ecosystem” of tools to be developed for building , simulating and analyzing these complex neuronal systems .']
['neuroscience', 'neuroscience/theoretical neuroscience', 'computational biology/systems biology', 'computational biology/computational neuroscience']