Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

User Tools

Site Tools


tutorials:start

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revisionBoth sides next revision
tutorials:start [2016/11/17 07:13] – [Advanced techniques: Extending the Auryn model corpus] zenketutorials:start [2017/01/29 17:09] – [Advanced techniques: Extending the Auryn model corpus] zenke
Line 12: Line 12:
  
 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.
- +  
-  * [[Creating a neuron model]]   +
   * [[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