NeuroML

Key takeaways

NeuroML belongs to biology; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect NeuroML to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of NeuroML from memory before moving on to harder problems.

Reference excerpt

NeuroML is an XML (Extensible Markup Language) based model description language that aims to provide a common data format for defining and exchanging models in computational neuroscience. The focus of NeuroML is on models which are based on the biophysical and anatomical properties of real neurons.

History The idea of creating NeuroML as a language for describing neuroscience models was first introduced by Goddard et al. (2001)

following meetings in Edinburgh where initial templates for the language structures were discussed. This initial proposal was based on general purpose structures proposed by Gardner et al. (2001).

At that time, the concept of NeuroML was closely linked with the idea of developing a software architecture in which a base application loads a range of plug-in components to handle different aspects of a simulation problem. Neosim (2003) was developed based on this goal, and early NeuroML development was closely aligned to this approach. Along with creating Neosim, Howell and Cannon developed a software library, the NeuroML Development Kit (NDK), to simplify the process of serializing models in XML. The NeuroML Development Kit implemented a particular dialect of XML, including the "listOfXXX" structure, which also found its way into SBML (Systems Biology Markup Language), but did not define any particular structures at the model description level. Instead, developers of plug-ins for Neosim were free to invent their own structures and serialize them via the NDK, in the hope that some consensus would emerge around the most useful ones. In practice, few developers beyond the Edinburgh group developed or used such structures and the resulting XML was too application specific to gain wider adoption. The Neosim project ended in 2005. Based on the ideas in Goddard et al. (2001) and discussions with the Edinburgh group, Sharon Crook began a collaborative effort to develop a language for describing neuronal morphologies in XML called MorphML. From the beginning, the idea behind MorphML was to develop a format for describing morphological structures that would include all of the necessary components to serve as a common data format with the added advantages of XML. At the same time, Padraig Gleeson and Angus Silver were developing neuroConstruct for generating neuronal simulations for the NEURON and GENESIS simulators. At that time, neuroConstruct utilized an internal simulator-independent representation for morphologies, channel and networks. It was agreed that these efforts should be merged under the banner of NeuroML, and the current structure of NeuroML was created. The schema was divided into levels (e.g. MorphML, ChannelML, and NetworkML) to allow different applications to support different part of the language.

Since 2006 the XML Schema files for this version of the standard have been available from the NeuroML development site.

The language

Aims The main aims of the NeuroML initiative are to:

To create specifications for a language (in XML) to describe the biophysics, anatomy and network architecture of neuronal systems at multiple scales To facilitate the exchange of complex neuronal network models between researchers, allowing for greater transparency and accessibility of models To promote software tools supporting NeuroML and to support the development of new software and databases To encourage researchers who create models within the scope of NeuroML to exchange and publish their models in this format.

Structure NeuroML is focused on biophysical and anatomical detailed models, i.e. incorporating real neuronal morphologies and membrane conductances (conductance based models), and network models based on known anatomical connectivity. The NeuroML structure is composed of Levels, where each Level deals with a particular biophysical scale. The modular nature of the specifications makes them easier to develop, understand, and use since one can focus on one module at a time; however, the modules are designed to fit together seamlessly. There are currently three Levels of NeuroML defined:

Level 1 focuses on the anatomical aspects of cells and consists of a schema for Metadata and the main MorphML schema. Tools which model the detailed neuronal morphologies (such as NeuronLand) can use the informations contained in this Level. Level 2 describes the biophysical properties of cells and also the properties of channel and synaptic mechanisms using ChannelML. Software which simulate neuronal spiking behaviour (such as NEURON and MOOSE) can use this Level of model description. Level 3 describes the positions of cell in space and the network connectivity. This kind of information in NetworkML can be used by software (such as CX3D and PCSIM) to exchange details on network architecture. Level 3 files containing cell morphology and connectivity can also be used by applications such as neuroConstruct for reproducing and analysing networks of conductance based cell models. Current schemas in readable form are available on the NeuroML specifications page.

Application support for NeuroML A list of software packages which support all or part of NeuroML is available on the NeuroML website.

Community NeuroML is an international, free and open community effort. The NeuroML Team implements the NeuroML specifications, maintains the website and the validator, organizes annual workshops and other events, and manages specific funding for coordinating the further development of NeuroML. Version 2.0 of the NeuroML language is being developed by the Specification Committees. NeuroML also participates in the International Neuroinformatics Coordinating Facility Program on Multiscale Modeling.

Worked examples

Example 1 — a first encounter with NeuroML

Start with the simplest possible case. Write down what NeuroML claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to NeuroML before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about NeuroML ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of NeuroML

In research: NeuroML appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses NeuroML in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: NeuroML is common in secondary-school and first-year university syllabi. It links to neighbouring topics Neuroinformatics, XML-based standards, so understanding it makes those chapters shorter.
In everyday life: Look for NeuroML outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study NeuroML in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what NeuroML means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain NeuroML out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is NeuroML in simple terms?

NeuroML is an XML (Extensible Markup Language) based model description language that aims to provide a common data format for defining and exchanging models in computational neuroscience. The focus of NeuroML is on models which are based on the biophysical and anatomical properties of real neurons.

Why does NeuroML matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study NeuroML?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on NeuroML.