Wiki source code of BluePyOpt

Version 13.1 by abonard on 2025/04/10 15:03
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adavison 1.1 1
abonard 3.1 2
3 * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
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abonard 6.1 5 * ((( ==== **[[Intermediate >>||anchor = "HIntermediate-1"]]** ==== )))
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abonard 11.1 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 4.1 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 4.1 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 5.1 21 === [[Optimising synaptic parameters>>https://github.com/BlueBrain/BluePyOpt/blob/master/examples/expsyn/ExpSyn.ipynb||rel=" noopener noreferrer" target="_blank"]] ===
abonard 4.1 22
abonard 5.1 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 6.1 26 === **Intermediate** ===
abonard 5.1 27
abonard 6.1 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 7.1 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 6.1 34
abonard 7.1 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 8.1 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 7.1 39
abonard 8.1 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 9.1 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 8.1 44
abonard 9.1 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 10.1 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 9.1 49
abonard 10.1 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 11.1 53 === **Advanced** ===
abonard 10.1 54
abonard 11.1 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 12.1 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 11.1 61
abonard 12.1 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 13.1 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 12.1 66
abonard 13.1 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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