At many scales in neuroscience, appropriate mathematical models take the form
of complex dynamical systems. Parametrising such models to conform to the
multitude of available experimental constraints is a global nonlinear
optimisation problem with a complex fitness landscape, requiring numerical
techniques to find suitable approximate solutions. Stochastic optimisation
approaches, such as evolutionary algorithms, have been shown to be effective,
but often the setting up of such optimisations and the choice of a specific
search algorithm and its parameters is non-trivial, requiring domain-specific
expertise. Here we describe BluePyOpt, a Python package targeted at the broad
neuroscience community to simplify this task. BluePyOpt is an extensible
framework for data-driven model parameter optimisation that wraps and
standardises several existing open-source tools. It simplifies the task of
creating and sharing these optimisations, and the associated techniques and
knowledge. This is achieved by abstracting the optimisation and evaluation
tasks into various reusable and flexible discrete elements according to
established best-practices. Further, BluePyOpt provides methods for setting up
both small- and large-scale optimisations on a variety of platforms, ranging
from laptops to Linux clusters and cloud-based compute infrastructures. The
versatility of the BluePyOpt framework is demonstrated by working through three
representative neuroscience specific use cases
