Arbor

• A simple single cell model (beginner)
• A detailed single cell model (advanced)
• A detailed single cell recipe (advanced)
• A ring network (advanced)
• A simple dendrite (advanced)
• A simple single cell recipe (advanced)
• A single cell model from the Allen Brain Atlas (advanced)
• A single cell model from the BluePyOpt Cell Optimisation Library (advanced)
• Distributed ring network (MPI) (advanced)
• Extracellular signals (LFPykit) (advanced)
• GPU and profiling (advanced)
• Spike Timing-dependent Plasticity Curve (advanced)
• Two cells connected via a gap junction (advanced)

Electrophysiology Analysis Toolkit

• Statistics of spike trains (beginner)
• ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains (advanced)
• Gaussian Process Factor Analysis (advanced)
• Parallel (advanced)
• Spike Pattern Detection and Evaluation (SPADE) (advanced)
• The Unitary Events Analysis (advanced)
• Time-domain Granger Causality (advanced)

NEST

• Introduction to NEST: Simulating a single neuron (beginner)
• Neurons and simple neural networks (beginner)
• Populations of neurons (beginner)
• Connect two NEST simulations using MUSIC (advanced)
• Connecting networks with synapses (advanced)
• Introduction to the MUSIC Interface (advanced)
• MUSIC Connections in C++ and Python (advanced)
• MUSIC with SLI (advanced)
• Practical tips (advanced)
• Spatially structured networks (advanced)
• The pymusic interface (advanced)

NESTML

• Izhikevich tutorial (beginner)
• Active dendrite tutorial (advanced)
• Dopamine-modulated STDP synapse tutorial (advanced)
• Ornstein-Uhlenbeck noise tutorial (advanced)
• STDP synapse tutorial (advanced)
• STDP windows tutorial (advanced)

Neuron

• Construction and Use of Models: Part 1. Elementary tools (beginner)
• The hoc programming language (beginner)
•  Introduction to Network Construction (advanced)
• 1. Implement and test the computational model itself (advanced)
• 2. Run a "complete" simulation and save its results (advanced)
• 3. Run a segmented simulation and save its results (advanced)
• 3D/Hybrid Intracellular Tutorial (advanced)
• 4. Reconstitute and verify the "complete" simulation results (advanced)
• A NEURON Programming Tutorial - Part A (advanced)
• A NEURON Programming Tutorial - Part B (advanced)
• A NEURON Programming Tutorial - part C (advanced)
• A NEURON Programming Tutorial - Part E (advanced)
• Ball and Stick model part 1 (advanced)
• Ball and Stick model part 2 (advanced)
• Ball and Stick model part 3 (advanced)
• Ball and Stick model part 4 (advanced)
• Creating a channel from a kinetic scheme specification (advanced)
• Creating a channel from an HH-style specification (advanced)
• Creating a model of stochastic channel gating (advanced)
• Creating a stylized ("stick figure") model cell (advanced)
• Dealing with simulations that generate a lot of data (advanced)
• Example: circadian rhythm (advanced)
• Example: restricting a reaction to part of a region (advanced)
• Exploring morphometric data and fixing problems (advanced)
• Extracellular Diffusion (advanced)
• How to generate independent random spike streams (advanced)
• Managing a model cell with complex anatomy (advanced)
• mGluR example (advanced)
• ModelView: Compact display of parameters for NEURON models. (advanced)
• Python introduction (advanced)
• Randomness in NEURON models (advanced)
• Reaction-Diffusion (advanced)
• Reaction-Diffusion: Calcium Wave (advanced)
• Reaction-Diffusion: Thresholds (advanced)
• Reaction-Diffusion: varying initial concentrations and parameters (advanced)
• Reading a morphometric data file and converting it to a NEURON model (advanced)
• RxD with MOD files (advanced)
• Scripting NEURON basics (advanced)
• Segmenting a simulation of a model cell (advanced)
• Segmenting a simulation of a model network (advanced)
• Source code that demonstrates the solution (advanced)
• Specifying parameterized variation of biophysical properties (advanced)
• The solution (advanced)
• Tutorial 1 : Fitting a function to data (advanced)
• Tutorial 1: Making Networks of Artificial Neurons (advanced)
• Tutorial 2 : Fitting a model to data (advanced)
• Tutorial 2: Making Hybrid Nets (advanced)
• Using Import3D (advanced)
• Using NEURON's Optimization Tools (advanced)

Neurorobotics Platform

• Drag and drop experiment set up with multiple robots (beginner)

PyNN

• Building and simulating a simple model (beginner)
• Installing PyNN on a Linux system (beginner)

The Virtual Brain

• Building Your Own Brain Network Model (beginner)
• Getting Started (beginner)
• My first simulation with The Virtual Brain (beginner)
• Population Models in The Virtual Brain (TVB) and the Phase Plane (beginner)
• Brain stimulation in The Virtual Brain (advanced)
• Clinical Applications - Stroke Recovery & Dementia (advanced)
• Evoked Responses in the Visual Cortex (advanced)
• Exploring a model (advanced)
• Hands-On: Modeling stroke brain (advanced)
• Human Brain Project (HBP) TVB-NEST co-simulation (advanced)
• Import Virtual Brain ready data into TVB and create a brain model (advanced)
• Integrated workflows: Image processing pipeline (advanced)
• Modeling brain dynamics in brain tumor patients using The Virtual Brain (advanced)
• Modeling Epilepsy (advanced)
• Modelling Epilepsy with The Virtual Brain (advanced)
• Region simulation tutorial (advanced)
• Resting State Networks (advanced)
• Simulating The Virtual Mouse Brain (TVMB) (advanced)
• Simulating The Virtual Mouse Brain (TVMB) (Copy) (advanced)
• Surface simulation tutorial (advanced)
• Surface-based simulations and deep brain stimulations (advanced)
• The Bayesian Virtual Epileptic Patient (BVEP) (advanced)
• TVB to NEST multi-scale simulation (advanced)

VoluBA

• Anchoring partial volumetric data to BigBrain using VoluBA (-)

ilastik

• Boundary-based segmentation with Multicut (advanced)
• Carving Workflow (advanced)
• Cell Density Counting Workflow (advanced)
• EBRAINS Atlases - QUINT workflow Tutorial (advanced)
• Object Classification (advanced)
• Pixel Classification workflow (advanced)
• Tracking MaMuT Export (advanced)
• Tracking workflow (advanced)