Abstract
A declarative extensible markup language (SpineML) for describing the dynamics, network and experiments of large-scale spiking neural network simulations is described which builds upon the NineML standard. It utilises a level of abstraction which targets point neuron representation but addresses the limitations of existing tools by allowing arbitrary dynamics to be expressed. The use of XML promotes model sharing, is human readable and allows collaborative working. The syntax uses a high-level self explanatory format which allows straight forward code generation or translation of a model description to a native simulator format. This paper demonstrates the use of code generation in order to translate, simulate and reproduce the results of a benchmark model across a range of simulators. The flexibility of the SpineML syntax is highlighted by reproducing a pre-existing, biologically constrained model of a neural microcircuit (the striatum). The SpineML code is open source and is available at http://bimpa.group.shef.ac.uk/SpineML.
Highlights
There is currently no clear consensus on the appropriate biological abstraction level for capturing the informationK
In the context of specification and simulation of networks of spiking point neurons it is important that modelling tools provide a level of abstraction w is computationally efficient to simulate but while still allowing a sufficient degree of flexibility to describe a wide range of neuronal phenomena
SpineML embraces this design goal by following the same layered specification which allows modular components describing neural dynamics to be expressed as differential equations rather than as solutions and by ensuring experimental details of a simulation are specified separately from other aspects of the model description
Summary
The model uses 2-variable point neurons with physiologically realistic attributes such a dopaminergic modulation (Humphries et al 2009a) and gap junctions (Humphries et al 2009b) Translating this model into a higher level modelling description facilitates sharing, portability, repeatability and collaboration. NineML was initiated by the INCFs Multiscale Modelling Program to address the limitations of both PyNN and NeuroML v.1 It bridges the gap between the fixed library of standard neuron types in PyNN and the focus of conductance-based compartmental cell models in NeuroML by allowing neurons with arbitrary dynamics and networks with arbitrary connectivity to be expressed in a declarative simulator independent format. The paper concludes by discussing the advantages of the SpineML format in comparison with alternative notations
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