brian2modelfitting¶
The package brian2modelfitting is a tool for parameter identification of
neuron models defined in the Brian 2 simulator.
Please report bugs at the GitHub issue tracker or at the Brian 2 discussion forum. The latter is also a place to discuss feature requests or potential contributions.
Model fitting¶
This toolbox allows the user to find the best fit of the unknown free parameters for recorded traces and spike trains. It also supports simulation-based inference, where instead of point-estimated parameter values, a full posterior distribution over the parameters is computed.
By default, the toolbox supports a range of global derivative-free
optimization methods, that include popular methods for model fitting:
differential evolution, particle swarm optimization and covariance matrix
adaptation (provided by the nevergrad, a gradient-free optimization
platform) as well as Bayesian optimization for black box functions (provided
by scikit-optimize, a sequential model-based optimization library). On the
other hand, simulation-based inference is the process of finding parameters of
a simulator from observations by taking a Bayesian approach, in our case, via
sequential neural posterior estimation, likelihood estimation or ratio
estimation (provided by the sbi), where neural densitiy estimator, a deep
neural network allowing probabilistic association between the data and
underlying parameter space, is trained. After the network is trained, the
approximated posterior distribution is available.
Just like Brian 2 simulator itself, the brian2modelfitting toolbox is
designed to be easy to use and to save time through automatic parallelization
of the simulations using code generation.