Tutorials & E-Library

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.

Given a computational model, we make it match a recorded waveform.

Given some experimental data, we approximate it with a function. This introduces material that will be helpful in the second tutorial.

This collection of tutorials shows how to use NEURON's optimization tools. Before working through these tutorials, most readers should probably examine the on-line "Introduction to Optimization" http://neos-guide.org/content/optimization-introduction

Those who have a special interest in the topic of optimization may wish to browse the NEOS Guide more extensively http://neos-guide.org/.

The general idea is that we want to adjust the parameters of a function or computational model so that its performance satisfies a set of criteria. These tutorials show how to accomplish this task by using the Multiple Run Fitter to
specify the function or model that we want to optimize
specify the criteria that we want it to satisfy
specify the parameters that will be adjusted,
and any constraints on those parameters
and finally
perform the optimization

Slides from a presentation on hoc syntax. Clear and concise. Includes an example of program analysis (walkthrough of code for a model cell generated by the CellBuilder).

Includes: an introduction to Python, how to work with NEURON through Python, how to develop a model from a single cell through a network.

This page demonstrates some basic Python concepts and essentials.

Here you can find information on how to use web app tools for 2D Image registration and analysis

The Hodgkin-Huxley Neuron Builder implements a Use Case of the Brain Simulation Platform. It allows the user to interactively go through the entire cell model building pipeline. The workflow consists of three steps: 1) electrophysiological feature extraction from voltage traces; 2) model parameter optimization; 3) in silico experiments using the optimized model cell. The user is provided with a friendly interface enabling to interact with both the HBP Collaboratory storage and the High Performance Computing (HPC) resources. The application has been built in a flexible way to allow the user to enter the workflow at any desired step, by either interacting with HBP resources or uploading his own files.

Some morphometric data files contain errors or require special attention for one reason or another.

First we bring up an Import3d tool, then specify the file that we want to read, take a quick look at what we got, and finally export the data as a NEURON model.

A convenient GUI tool for converting Eutectic, Neurolucida, and swc morphometric data into NEURON models.

These tutorials are about NEURON's Import3D tool, which can translate common varieties of cellular morphometric data into a CellBuilder that specifies the anatomical properties of a model neuron. The capabilities of this tool are still evolving, and so are these tutorials.