NineML: declarative, mathematically-explicit descriptions of spiking neuronal networks
Boston University, USA
Texttensor Limited, United Kingdom
Georgia State University, USA
Katholieke Universiteit Leuven, Belgium
Okinawa Institute of Science and Technology, Japan
UCL, United Kingdom
Yale University, USA
Max Planck Institute for Dynamics and Self-Organization, Germany
University of Antwerp, Belgium
National Tsing Hua University, Taiwan
EPFL, BBP, Switzerland
Norwegian University of Life Sciences, Norway
National Center for Biological Sciences, India
University of Rostock, Germany
Brain Corporation, USA
The growing number of spiking neuronal network models has created a need for standards and guidelines to simplify model sharing and facilitate the reproduction of scientific results across different simulators. To coordinate and promote community efforts towards such standards, the International Neuroinformatics Coordinating Facility (INCF) has formed its Multiscale Modeling Program, and has assembled a task force to propose a declarative computer language for descriptions of spiking neuronal network models, called NineML, short for Network Interchange for Neuroscience Modeling Language.
NineML is divided into two semantic layers: the Abstraction Layer, which consists of core mathematical concepts necessary to express neuronal and synaptic dynamics and network connectivity patterns, and the User Layer, which provides constructs to specify the instantiation of a network model, based on the key concepts of network modeling (i.e. spiking neurons, synapses, populations of neurons and connectivity patterns across populations of neurons). Although initially NineML is focused on point neuron network models, the design developed by the Task Force is extensible and allows integration of additional models and concepts in later versions.
As part of the Abstraction Layer, NineML includes a flexible block diagram notation for describing spiking dynamics. This block diagram notation provides support to represent continuous and discrete variables, their evolution according to a set of rules such as a system of ordinary differential equations, and the conditions that induce a regime change, such as the transition from subthreshold mode to spiking and refractory modes. The task force has implemented several libraries for generating efficient numerical code from such descriptions.
In addition, the Abstraction Layer is being extended with set and graph operations to describe a variety of topographical arrangements of neurons and synapses, and to describe connectivity patterns between neuronal populations. The principal idea is to use interval sets and connectivity matrix patterns to express a vast range of network models with a concise notation and efficient implementation.
The User Layer provides means for specifying the structure of the elements of a spiking neuronal network. This includes parameters for each of the individual elements (cells, synapses, inputs) and the grouping of these entities into networks. In addition, the user layer defines the syntax for supplying parameter values to abstract connectivity patterns.
The NineML specification is defined as an implementation-neutral object model representing all the concepts in the User and Abstraction Layers. Libraries for creating, manipulating, querying and serializing the NineML object model to a standard XML representation will be delivered for a variety of languages. The task force aims to deliver at least two independently developed software implementations to support a wide range of numerical simulation platforms: NEURON, NEST, Brian, MOOSE, GENESIS-3, PCSIM, PyNN, Matlab, etc.
These implementations will allow simulator developers to quickly add support for NineML, and will thus catalyze the emergence of a broad software ecosystem supporting model definition interoperability around NineML.
4th INCF Congress of Neuroinformatics, Boston, USA, 4 Sep - 6 Sep, 2011.
(2011). NineML: declarative, mathematically-explicit descriptions of spiking neuronal networks.
4th INCF Congress of Neuroinformatics.
17 Oct 2011;
19 Oct 2011.
Dr. Anatoli Gorchetchnikov, Boston University, Boston, USA, email@example.com