LibNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience.

Michael Vella,Padraig Gleeson,Sharon Crook,R Angus Silver,Robert C Cannon,Gautham Ganapathy,Hugh P C Robinson,Andrew P Davison

doi:10.3389/fninf.2014.00038

Abstract

NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment.

Highlights

In neuroscience, models based on detailed anatomy and electrophysiology have been used for many years to help explore and understand neural systems
Python libraries for reading, writing and manipulating models in these languages are an important step toward encouraging the wider use of these languages
List 1 | Example of loading a NeuroML multicompartmental cell morphology with libNeuroML and using helper functions to calculate some of the properties (area and volume) of the cell

Summary

Introduction

Models based on detailed anatomy and electrophysiology have been used for many years to help explore and understand neural systems These models have been expressed using a variety of programming languages, tools, and techniques, leading to a high degree of fragmentation (Cannon et al, 2007). Domain-specific modeling languages have been developed to address this fragmentation, aid with model exchange, and provide language features, such as built-in methods and classes, which simplify modeling in that particular domain. This is achieved by formalizing common concepts with a standardized set of language expressions and rules. In the latest version of NeuroML (version 2.0 or v2), the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities (Cannon et al, 2012)

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Conclusion