Simulator for spiking neural networks with synaptic plasticity https://zenkelab.org/auryn/ 2026-04-18T12:11:11+00:00 https://zenkelab.org/auryn/ https://zenkelab.org/auryn/lib/exe/fetch.php?media=wiki:logo.png text/html 2016-08-18T16:58:36+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=tutorials:multiple_synaptic_state_variables&rev=1471539516&do=diff Tutorial: Multiple Synaptic State Variables Let's assume you would like to write a plasticity model in which induced changes to a synapse require some time to percolate through. Consider that inserting for instance additional AMPA receptors into a postsynaptic density takes time text/html 2016-08-18T17:00:00+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=tutorials:old_complex_synapse_tutorial&rev=1471539600&do=diff Implementing multiple synaptic states (the old way) Note, that starting with Auryn v0.7 there is a new (and better way) of dealing with multiple synaptic state variables. Aims Our aim is to introduce a meta-variable lpw which characterizes synaptic strength as it is transmitted. It is a low-pass filtered version of the plastic weight text/html 2018-05-30T07:21:44+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=tutorials:start&rev=1527664904&do=diff Tutorials 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 compile and run). * text/html 2017-04-24T19:16:01+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=tutorials:tutorial_1&rev=1493061361&do=diff Tutorial 1: Single neuron with Poisson input Here you will learn to simulate a single AdEx neuron and record spikes and membrane potentials. Bare bones of an Auryn simulation First create a new file named sim_mysolution1.cpp and make it a regular C++ program: text/html 2018-02-28T17:58:36+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=tutorials:tutorial_2&rev=1519840716&do=diff Tutorial 2: Balanced network Here you will learn to wire up a simple balanced network. This assumes that you already know the basics explained in Tutorial 1. The code of this example can be found here <https://github.com/fzenke/auryn/blob/master/examples/sim_tutorial2.cpp> Setting up neural populations A balanced network has an excitatory and an inhibitory population which we model as distinct text/html 2016-09-02T04:46:52+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=tutorials:tutorial_3&rev=1472791612&do=diff Tutorial 3: Balanced network with synaptic plasticity (triplet STDP) Here you will learn to extend the balanced network model we hacked together in Tutorial 2 with plastic synapses. As you have seen in the previous section the firing rate distribution is relatively wide in our random network. Moreover firing rates are pretty high. If you have tried to tune the weights such that the network exhibits a more plausible activity level, you might have noticed that this not completely trivial and th… text/html 2019-07-29T15:02:08+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=tutorials:tutorial_4&rev=1564412528&do=diff Tutorial 4 For an intro to supervised learning with SuperSpike in temporally coding spiking neural networks please refer to the example code here <https://github.com/fzenke/pub2018superspike> text/html 2018-02-07T23:11:16+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=tutorials:writing_your_own_plasticity_model&rev=1518045076&do=diff Synapse and Plasticity Models Auryn was written with the aim to simplify the development of spike-timing-dependent plasticity (STDP) models and to run them efficiently in distributed network simulations. Behind the scenes Auryn diverts from existing standard simulators (like NEST) in that it has the inbuilt functionality to back-propagate action potentials to the presynaptic neurons. This largely simplifies implementing plasticity models which can be written as the product of