ArticleFrontiers in neuroinformatics2016
BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.
Article in Frontiers in neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers.
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114 citing papers in PubMed.
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- eFEL: electrophysiology feature extraction library.Bioinformatics (Oxford, England) · 2026Article
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- Visualization and simulation of full-scale point-neuron circuits via the Neural Circuit Visualizer web platform.Scientific reports · 2026Article
- Persistent adaptation through dual-timescale regulation of ion channel properties.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Model-based design of subthalamic nucleus neurons using hybrid optimization.Journal of neurophysiology · 2026Article
- Modeling and simulation of neocortical micro- and mesocircuitry (Part II, Physiology and experimentation).eLife · 2026Article
- How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells.Frontiers in computational neuroscience · 2026Article
- Synaptic integration and competition in the substantia nigra pars reticulata-An experimental and in silico analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- A unified model library maps how neuromodulation reshapes the excitability landscape of neurons across the brain.PLoS computational biology · 2025Article
- WhenAPL bioengineering · 2025Review
- JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.Nature methods · 2025Article
- Persistent Adaptation through Dual-Timescale Regulation of Ion Channel Properties.bioRxiv : the preprint server for biology · 2025Article
- Higher-order interactions in neuronal function: From genes to ionic currents in biophysical models.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Mouse and human striatal projection neurons compared - somatodendritic arbor, spines and in silico analyses.PLoS computational biology · 2025Article
- Computer models predict differential dendritic vulnerability with ischemia and spreading depression.bioRxiv : the preprint server for biology · 2025Article
54 more citing papers are in PubMed but not listed here.
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9 authors.
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Abstract
At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear 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 standardizes 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.
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