Andrew P. Davison

TL;DR: In this paper, a review of different aspects of the simulation of spiking neural networks is presented, with the aim of identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking networks.

TL;DR: PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools.

TL;DR: The development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.

TL;DR: The NEURON simulation program now allows Python to be used, alone or in combination withNEURON's traditional Hoc interpreter, and the use of the xml module in implementing NEurON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.

TL;DR: This work proposes here a language-independent object model, named “Neo,” suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations, and develops an open source implementation in the Python programming language that should become the standard basis for Python tools in neurophysiology.