Wiki source code of BluePyOpt

Last modified by abonard on 2025/06/13 16:05
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adavison 1.1 1
abonard 3.1 2
3 * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
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abonard 50.1 5 * ((( ==== **[[Intermediate >>||anchor = "HIntermediate-1"]]** ==== )))
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abonard 55.1 7 * ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
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abonard 3.1 9 === **Beginner** ===
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jessicamitchell 2.1 11 === [[Creating a simple cell optimisation>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
adavison 1.1 12
abonard 3.1 13 **Level**: beginner(%%) **Type**: interactive tutorial
jessicamitchell 2.1 14
abonard 3.1 15 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.
abonard 48.1 16 === [[Optimisation using the CMA evolutionary strategy>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/cma_strategy/cma.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
jessicamitchell 2.1 17
abonard 48.1 18 **Level**: beginner(%%) **Type**: interactive tutorial
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20 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.
abonard 49.1 21 === [[Optimising synaptic parameters>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/expsyn/ExpSyn.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
abonard 48.1 22
abonard 49.1 23 **Level**: beginner(%%) **Type**: interactive tutorial
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25 This notebook shows how the parameters of a NEURON point process (in this case a synapse), can be optimised using BluePyOpt.
abonard 50.1 26 === **Intermediate** ===
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abonard 50.1 28 === [[Creating an optimisation with meta parameters>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/metaparameters/metaparameters.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
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30 **Level**: intermediate(%%) **Type**: interactive tutorial
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32 This notebook will explain how to set up an optimisation that uses metaparameters (parameters that control other parameters)
abonard 51.1 33 === [[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"]] ===
abonard 50.1 34
abonard 51.1 35 **Level**: intermediate(%%) **Type**: interactive tutorial
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37 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).
abonard 52.1 38 === [[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"]] ===
abonard 51.1 39
abonard 52.1 40 **Level**: intermediate(%%) **Type**: interactive tutorial
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42 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.
abonard 53.1 43 === [[Tsodyks-Markram model of short-term synaptic plasticity>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
abonard 52.1 44
abonard 53.1 45 **Level**: intermediate(%%) **Type**: interactive tutorial
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47 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).
abonard 54.1 48 === [[Optimization of burst and tonic firing in thalamo-cortical neurons>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/thalamocortical-cell/thalamocortical-cell_opt.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
abonard 53.1 49
abonard 54.1 50 **Level**: intermediate(%%) **Type**: interactive tutorial
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52 In this tutorial we will go over how to set up the cell model and the cell evaluator, run an optimisation and how to analyse optimisation results.
abonard 55.1 53 === **Advanced** ===
abonard 54.1 54
abonard 55.1 55 === [[Optimisation of a Neocortical Layer 5 Pyramidal Cell>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
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57 **Level**: advanced(%%) **Type**: interactive tutorial
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59 This notebook shows you how to optimise the maximal conductance of Neocortical Layer 5 Pyramidal Cell as used in Markram et al. 2015.
abonard 56.1 60 === [[Optimisation of a Neocortical Layer 5 Pyramidal Cell in Arbor>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC_arbor.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
abonard 55.1 61
abonard 56.1 62 **Level**: advanced(%%) **Type**: interactive tutorial
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64 This notebook shows you how to optimise the maximal conductance of Neocortical Layer 5 Pyramidal Cell as used in Markram et al. 2015 using Arbor as the simulator.
abonard 57.1 65 === [[Simulating optimized cell models in Arbor and cross-validation with Neuron>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/l5pc_validate_neuron_arbor.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
abonard 56.1 66
abonard 57.1 67 **Level**: advanced(%%) **Type**: interactive tutorial
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69 This notebook demonstrates how to run a simulation of a simple single compartmental cell with fixed/optimized parameters in Arbor. We follow the standard BluePyOpt flow of setting up an electrophysiological experiment and export the cell model to a mixed JSON/ACC-format. We then cross-validate voltage traces obtained with Arbor with those from a Neuron simulation.
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