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This article is part of the supplement: Twenty First Annual Computational Neuroscience Meeting: CNS*2012

Open Access Open Badges Poster presentation

Exploring the functional implications of brain architecture and connectivity: a multi-simulator framework for biophysical neuronal models

Thomas G Close1*, Ivan Raikov12, Mario Negrello1, Shyam Kumar12 and Erik De Schutter12

Author Affiliations

1 Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, Japan

2 University of Antwerp, Antwerp, Belgium

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BMC Neuroscience 2012, 13(Suppl 1):P150  doi:10.1186/1471-2202-13-S1-P150

The electronic version of this article is the complete one and can be found online at:

Published:16 July 2012

© 2012 Close et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

We introduce a framework for implementing networks of neuronal models with conductance-based mechanisms and morphology (where applicable) across multiple simulators. The framework extends the existing NINEML language [1] by adding two independent modules, NINEML-Conductance and NINEML-BREP [2], which allow the specification of conductance-based mechanisms and geometrically derived connectivity respectively. The PyNN API [3] is utilised to reproduce connectivity across multiple simulators, with adapters added where necessary to accommodate the proposed extensions to NINEML.

PyNN was chosen to handle the multi-simulator connectivity because it offers translations to a wide range of neural simulators and provides a standardised Python interface for simulation control. It is also straightforward to load predefined connectivity into the PyNN-Connector API from a sparse-matrix-like format, allowing a general interface to NINEML-BREP.

Neuronal mechanisms are precompiled into simulator-dependent formats from the NINEML-Conductance declaration, and are then integrated into PyNN via a novel “conductance standard model” class. Depending on whether the selected simulator supports multi-compartment neuronal models, cell morphology is optionally loaded from the NINEML-BREP description and incorporated into the conductance standard model, with flags set in the declarative model description to handle the required adjustments to mechanism parameters.

By the meeting we aim to have completed the extensions to the NINEML language and the required interface between the extended NINEML language and PyNN for the NEURON [4] and NEST [5] simulators, and have a working network model of the cerebellar cortex within this framework. This will enable us to test the effect of varying the biophysical detail of neuronal models and different simulators on the proposed cerebellar cortex model.


  1. NINEML [] webcite

  2. Negrello M, Raikov I, De Schutter E: Boundary representation of neural architecture and connectivity.

    BMC Neurosci 2011, 12(Suppl 1):59. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  3. Davison AP, Brüderle D, Eppler JM, Kremkow J, Muller E, Pecevski DA, Perrinet L, Yger P: PyNN: a common interface for neuronal network simulators.

    Front. Neuroinform 2008, 2:11. PubMed Abstract | PubMed Central Full Text OpenURL

  4. Carnevale NT, Hines ML: The NEURON Book. Cambridge Univ Pr; 2006.

  5. Gewaltig M-O, Diesmann M: NEST (Neural Simulation Tool).

    Scholarpedia 2007, 2(4):1430. Publisher Full Text OpenURL