Event Abstract

Back to Event

NineML: declarative, mathematically-explicit descriptions of spiking neuronal networks

Anatoli Gorchetchnikov1*, Robert Cannon2, Robert Clewley3, Hugo Cornelis4, Andrew Davison5, Erik De Schutter6, Mikael Djurfeldt7, Padraig Gleeson8, Sean Hill7, Mike Hines9, Birgit Kriener10, Yann Le Franc11, Chung-Chuan Lo12, Abigail Morrison13, Eilif Muller14, Hans Ekkehard Plesser15, Ivan Raikov6, Subhasis Ray16, Lars Schwabe17 and Botond Szatmary18
  • 1 Boston University, United States
  • 2 Texttensor Limited, United Kingdom
  • 3 Georgia State University, United States
  • 4 Katholieke Universiteit Leuven, Belgium
  • 5 CNRS, France
  • 6 Okinawa Institute of Science and Technology, Japan
  • 7 INCF, Sweden
  • 8 UCL, United Kingdom
  • 9 Yale University, United States
  • 10 Max Planck Institute for Dynamics and Self-Organization, Germany
  • 11 University of Antwerp, Belgium
  • 12 National Tsing Hua University, Taiwan
  • 13 RIKEN, Japan
  • 14 EPFL, BBP, Switzerland
  • 15 Norwegian University of Life Sciences, Norway
  • 16 National Center for Biological Sciences, India
  • 17 University of Rostock, Germany
  • 18 Brain Corporation, United States

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.

Keywords: General neuroinformatics, Neuroimaging

Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011.

Presentation Type: Poster Presentation

Topic: General neuroinformatics

Citation: Gorchetchnikov A, Cannon R, Clewley R, Cornelis H, Davison A, De Schutter E, Djurfeldt M, Gleeson P, Hill S, Hines M, Kriener B, Le Franc Y, Lo C, Morrison A, Muller E, Plesser H, Raikov I, Ray S, Schwabe L and Szatmary B (2011). NineML: declarative, mathematically-explicit descriptions of spiking neuronal networks. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00098

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 17 Oct 2011; Published Online: 19 Oct 2011.

* Correspondence: Dr. Anatoli Gorchetchnikov, Boston University, Boston, United States, anatoli@bu.edu