Hide last authors
| author | version | line-number | content |
|---|---|---|---|
 |
1.1 | 1 | |
| |
3.1 | 2 | |
| 3 | * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== ))) | ||
| 4 | |||
| |
28.1 | 5 | * ((( ==== **[[Intermediate >>||anchor = "HIntermediate-1"]]** ==== ))) |
| 6 | |||
| |
3.1 | 7 | === **Beginner** === |
| 8 | |||
| |
2.1 | 9 | === [[Creating a simple cell optimisation>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb||rel=" noopener noreferrer" target="_blank"]] === |
| |
1.1 | 10 | |
| |
3.1 | 11 | **Level**: beginner(%%) **Type**: interactive tutorial |
| |
2.1 | 12 | |
| |
3.1 | 13 | 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. |
| |
26.1 | 14 | === [[Optimisation using the CMA evolutionary strategy>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/cma_strategy/cma.ipynb||rel=" noopener noreferrer" target="_blank"]] === |
| |
2.1 | 15 | |
| |
26.1 | 16 | **Level**: beginner(%%) **Type**: interactive tutorial |
| 17 | |||
| 18 | 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. | ||
| |
27.1 | 19 | === [[Optimising synaptic parameters>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/expsyn/ExpSyn.ipynb||rel=" noopener noreferrer" target="_blank"]] === |
| |
26.1 | 20 | |
| |
27.1 | 21 | **Level**: beginner(%%) **Type**: interactive tutorial |
| 22 | |||
| 23 | This notebook shows how the parameters of a NEURON point process (in this case a synapse), can be optimised using BluePyOpt. | ||
| |
28.1 | 24 | === **Intermediate** === |
| |
27.1 | 25 | |
| |
28.1 | 26 | === [[Creating an optimisation with meta parameters>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/metaparameters/metaparameters.ipynb||rel=" noopener noreferrer" target="_blank"]] === |
| 27 | |||
| 28 | **Level**: intermediate(%%) **Type**: interactive tutorial | ||
| 29 | |||
| 30 | This notebook will explain how to set up an optimisation that uses metaparameters (parameters that control other parameters) | ||
| |
29.1 | 31 | === [[Setup of a cell model with multi electrode simulation for local field potential recording>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc_lfpy/L5PC_LFPy.ipynb||rel=" noopener noreferrer" target="_blank"]] === |
| |
28.1 | 32 | |
| |
29.1 | 33 | **Level**: intermediate(%%) **Type**: interactive tutorial |
| 34 | |||
| 35 | 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). | ||
| |
30.1 | 36 | === [[Exporting a cell in the neuroml format and running it>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/neuroml/neuroml.ipynb||rel=" noopener noreferrer" target="_blank"]] === |
| |
29.1 | 37 | |
| |
30.1 | 38 | **Level**: intermediate(%%) **Type**: interactive tutorial |
| 39 | |||
| 40 | In this tutorial we will go over how to export a cell to neuroml, create a LEMS simulation able to run the neuroml cell and then how to run the simulation. | ||
| 41 |