Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

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tutorials:start [2017/01/29 17:09] – [Advanced techniques: Extending the Auryn model corpus] zenketutorials:start [2018/05/30 07:20] – Changes list zenke
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   * [[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]]: Build a simple balanced network model. +  * [[Tutorial 2]]: Build the Vogels & Abbott balanced network model. 
   * [[Tutorial 3]]: Create and simulate a network model with inhibitory plasticity.   * [[Tutorial 3]]: Create and simulate a network model with inhibitory plasticity.
 +  * [[Tutorial 4]]: [[SuperSpike]]: Supervised learning in spiking networks (coming soon).
  
  
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 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. 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.
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   * [[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.
  
  
tutorials/start.txt · Last modified: 2018/05/30 07:21 by zenke