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At many scales in neuroscience, appropriate mathematical models take the formof complex dynamical systems. Parametrising such models to conform to themultitude of available experimental constraints is a global nonlinearoptimisation problem with a complex fitness landscape, requiring numericaltechniques to find suitable approximate solutions. Stochastic optimisationapproaches, such as evolutionary algorithms, have been shown to be effective,but often the setting up of such optimisations and the choice of a specificsearch algorithm and its parameters is non-trivial, requiring domain-specificexpertise. Here we describe BluePyOpt, a Python package targeted at the broadneuroscience community to simplify this task. BluePyOpt is an extensibleframework for data-driven model parameter optimisation that wraps andstandardises several existing open-source tools. It simplifies the task ofcreating and sharing these optimisations, and the associated techniques andknowledge. This is achieved by abstracting the optimisation and evaluationtasks into various reusable and flexible discrete elements according toestablished best-practices. Further, BluePyOpt provides methods for setting upboth small- and large-scale optimisations on a variety of platforms, rangingfrom laptops to Linux clusters and cloud-based compute infrastructures. Theversatility of the BluePyOpt framework is demonstrated by working through threerepresentative neuroscience specific use cases.

BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience