Beginner
Creating a simple cell optimisation
Level: beginner Type: interactive tutorial
This notebook will explain how to set up an optimisation of simple single compartmental cell with two free parameters that need to be optimised. As this optimisation is for example purpose only, no real experimental data is used in this notebook.
Optimisation using the CMA evolutionary strategy
Level: beginner Type: interactive tutorial
This notebook will explain how to optimise a model using the covariance matrix adaptation (CMA) optimisation strategy. BluePyOpt includes two flavors of CMA: a single objective one and a hybrid single/multi objective one.
Optimising synaptic parameters
Level: beginner Type: interactive tutorial
This notebook shows how the parameters of a NEURON point process (in this case a synapse), can be optimised using BluePyOpt.
Intermediate
Creating an optimisation with meta parameters
Level: intermediate Type: interactive tutorial
This notebook will explain how to set up an optimisation that uses metaparameters (parameters that control other parameters)
Setup of a cell model with multi electrode simulation for local field potential recording
Level: intermediate Type: interactive tutorial
This notebook will demonstrate how to instantiate a cell model and evaluator that include local field potential (LFP) computation and its recording using a simulated multi electrode array (MEA).