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Introduction

The Blue Brain Python Optimisation Library (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.

External use cases

• Using BluePyOpt to optimise BRIAN 2 models by Marcel Stimberg: https://gist.github.com/mstimberg/fc202e19382d2344fb0b7c4b2d24a758
• Using BluePyOpt to optimise PyNN / NEST models by Andrew Davison: https://github.com/apdavison/BluePyOpt/tree/pynn-models/bluepyopt/ephys_pyNN

Publications that use or mention BluePyOpt

The list of publications that use or mention BluePyOpt can be found here.