| ... |
... |
@@ -2,8 +2,6 @@ |
| 2 |
2 |
|
| 3 |
3 |
* ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== ))) |
| 4 |
4 |
|
| 5 |
|
-* ((( ==== **[[Intermediate >>||anchor = "HIntermediate-1"]]** ==== ))) |
| 6 |
|
- |
| 7 |
7 |
=== **Beginner** === |
| 8 |
8 |
|
| 9 |
9 |
=== [[Creating a simple cell optimisation>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb||rel=" noopener noreferrer" target="_blank"]] === |
| ... |
... |
@@ -16,31 +16,4 @@ |
| 16 |
16 |
**Level**: beginner(%%) **Type**: interactive tutorial |
| 17 |
17 |
|
| 18 |
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. |
| 19 |
|
-=== [[Optimising synaptic parameters>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/expsyn/ExpSyn.ipynb||rel=" noopener noreferrer" target="_blank"]] === |
| 20 |
20 |
|
| 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. |
| 24 |
|
-=== **Intermediate** === |
| 25 |
|
- |
| 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) |
| 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"]] === |
| 32 |
|
- |
| 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). |
| 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"]] === |
| 37 |
|
- |
| 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 |
|
-=== [[Tsodyks-Markram model of short-term synaptic plasticity>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp.ipynb||rel=" noopener noreferrer" target="_blank"]] === |
| 42 |
|
- |
| 43 |
|
-**Level**: intermediate(%%) **Type**: interactive tutorial |
| 44 |
|
- |
| 45 |
|
-In this notebook we demonstrate how to fit the parameters of the Tsodyks-Markram model to a given in vitro somatic recording. The in vitro trace used here shows a typical L5TTPC-L5TTPC depressing connection, kindly provided by Rodrigo Perin (EPFL). |
| 46 |
|
- |
