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--- tutorials:start [2016/09/01 23:30] – [Starting out with off the shelf models] list order zenke
+++ tutorials:start [2018/05/30 07:21] (current) – Changes link zenke
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 ====== Tutorials ======
-Auryn comes with a variety of neuron and plasticity models (see [[manual:start]]). However, in most cases you will want to use and simulate your own models. Auryn is written in a modular way which greatly simplifies the process of creating your own neuron models ([[manual:NeuronGroup]]), synapse and plasticity models ([[manual:Connection]]) and devices or tools to interact with the simulation. In this section we will slowly build several example walk-throughs of how to achieve this.
+The following tutorials provide a step-by-step introduction to some off the shelf models. To be able to follow this tutorial we will assume you have already compiled Auryn and you know how to run  your own Auryn simulations (see [[:quick start]] and [[manual:compileandrunaurynsimulations|compile and run]]).
-===== Starting out with off the shelf models =====
-First see how to [[manual:compileandrunaurynsimulations|compile and run]] your own Auryn simulations.
   * [[Tutorial 1]]: Simulate a single AdEx neuron and record spikes and membrane potentials.
-  * [[Tutorial 2]]: A simple balanced network model.
+  * [[Tutorial 2]]: Build the Vogels & Abbott balanced network model.
-  * [[Tutorial 3]]: A network model with inhibitory plasticity.
+  * [[Tutorial 3]]: Create and simulate a network model with inhibitory plasticity.
-  * See [[:quick start]] for some more first examples
+  * [[Tutorial 4]]: [[manual:SuperSpike]]: Supervised learning in spiking networks (coming soon).
 ===== Advanced techniques: Extending the Auryn model corpus =====
+As you have seen Auryn already comes with a variety of neuronal and synaptic plasticity models as well as devices to interact with and to record from your network simulations (see [[manual:start]]). However, in many cases you will want to create your own models. Auryn is written in a modular way which greatly simplifies the process. In this section you will learn step-by-step how.
   * [[Writing your own plasticity model]]. This is a simple walk-through for the logic behind plastic updates and what methods are called where and when. It sketches in simple terms what needs to be done to implement a new custom synapse model in Auryn.
   * [[Multiple synaptic state variables]]. This example aims at creating a plastic connection object in which the the actual weight change is the low-pass filtered output of meta-variable which is influenced by STDP.
-  * Neuron Models: Coming soon.