tutorials:start
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tutorials:start [2016/09/01 23:30] – [Starting out with off the shelf models] list order zenke | tutorials:start [2017/12/13 15:26] – Adds SuperSpike teaser zenke | ||
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====== Tutorials ====== | ====== Tutorials ====== | ||
- | Auryn comes with a variety of neuron and plasticity | + | The following tutorials provide |
- | + | ||
- | ===== Starting out with off the shelf models ===== | + | |
- | + | ||
- | First see how to [[manual: | + | |
* [[Tutorial 1]]: Simulate a single AdEx neuron and record spikes and membrane potentials. | * [[Tutorial 1]]: Simulate a single AdEx neuron and record spikes and membrane potentials. | ||
- | * [[Tutorial 2]]: A simple | + | * [[Tutorial 2]]: Build the Vogels & Abbott |
- | * [[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]]: SuperSpike: Supervised learning in spiking networks (coming soon). |
===== Advanced techniques: Extending the Auryn model corpus ===== | ===== 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: | ||
* [[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. | * [[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. | * [[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. | ||
tutorials/start.txt · Last modified: 2018/05/30 07:21 by zenke