BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience

  title={BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience},
  author={Werner Van Geit and Michael Gevaert and Giuseppe Chindemi and Christian A. R{\"o}ssert and Jean-Denis Courcol and Eilif B. M{\"u}ller and Felix Sch{\"u}rmann and Idan Segev and Henry Markram},
  journal={Frontiers in Neuroinformatics},
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… 

Figures and Tables from this paper

A unified framework for the application and evaluation of different methods for neural parameter optimization

A software tool for fitting the parameters of neural models is developed, which provides intuitive, uniform access to a variety of state-of-the-art optimization algorithms implemented by four different Python packages, and the performance of several evolutionary and related algorithms is evaluated.

Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn

L2L is presented as an easy to use and flexible framework to perform hyper-parameter space exploration of neuroscience models on HPC infrastructure.

pypet: A Python Toolkit for Data Management of Parameter Explorations

pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations that promotes reproducible research in computational neuroscience and simulation-based disciplines.

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

These efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell

This article extends a recent methodological workflow for creating realistic and computationally efficient neuron models whilst capturing essential aspects of single-neuron dynamics by proposing an alternative optimization component based on multimodal algorithms.

Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience

The aim of the current paper is to present Uncertainpy, an open-source Python toolbox tailored to perform uncertainty quantification and sensitivity analysis of neuroscience models, for the neuroscience community in a user- oriented manner.

NeuronUnit: A package for data-driven validation of neuron models using SciUnit

NeuronUnit is described, a library that builds upon SciUnit and integrates with several existing neuroinformatics resources to support the validation of single-neuron models using data gathered by neurophysiologists and neuroanatomists.

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models

The EBRAINS Hodgkin-Huxley Neuron Builder is a web resource for building single cell neural models via the extraction of activity features from electrophysiological traces, the optimization of the model parameters via a genetic algorithm executed on high performance computing facilities and the simulation of the optimized model in an interactive framework.

Data driven building of realistic neuron model using IBEA and CMA evolution strategies

To the authors knowledge, this is the first time that multi-objective covariance matrix adaptation evolution strategy is used in such a highly-dimensional parameter space using e-features obtained from experimental recordings.

NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs

A simulation environment that takes advantage of the inherent parallelized structure of graphics processing unit (GPU) to accelerate neuronal simulation and enables the rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.



A flexible, interactive software tool for fitting the parameters of neuronal models

Detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

Automated neuron model optimization techniques: a review

This review separates three types of error functions: feature-based ones, point-by-point comparison of voltage traces and multi-objective functions, and several popular search algorithms, including brute-force methods, simulated annealing, genetic algorithms, evolution strategies, differential evolution and particle-swarm optimization.

Neurofitter: A Parameter Tuning Package for a Wide Range of Electrophysiological Neuron Models

Results obtained by applying Neurofitter to a simple single compartmental model and a complex multi-compartmental Purkinje cell (PC) model are shown to show that the method is able to solve a variety of tuning problems and demonstrate details of its practical application.

LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2

LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties.

A Comparative Survey of Automated Parameter-Search Methods for Compartmental Neural Models

Comparing the performance of four different parameter-search methods on several single-neuron models demonstrates that genetic algorithms and simulated annealing are generally the most effective methods.

PyNEST: A Convenient Interface to the NEST Simulator

PyNEST, the new user interface to NEST, combines NEST's efficient simulation kernel with the simplicity and flexibility of Python, and makes it easier to set up simulations, generate stimuli, and analyze simulation results.

PyMOOSE: Interoperable Scripting in Python for MOOSE

This work shows how the Python-scripting version of MOOSE, PyMOOSE, combines the power of a compiled simulator with the versatility and ease of use of Python, and has a high degree of interoperability with analysis routines, with graphical toolkits, and with other simulators.

CyNEST: a maintainable Cython-based interface for the NEST simulator

This contribution presents the re-implementation of PyNEST in the Cython language, a superset of Python that additionally supports the declaration of C/C++ types for variables and class attributes, and provides a convenient foreign function interface (FFI) for invoking C-C++ routines (Behnel et al., 2011).

PyNN: A Common Interface for Neuronal Network Simulators

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.