Volume 13 Supplement 1
Exploring the functional implications of brain architecture and connectivity: a multi-simulator framework for biophysical neuronal models
© Close et al; licensee BioMed Central Ltd. 2012
Published: 16 July 2012
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  by adding two independent modules, NINEML-Conductance and NINEML-BREP , which allow the specification of conductance-based mechanisms and geometrically derived connectivity respectively. The PyNN API  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.
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