Simulator for spiking neural networks with synaptic plasticity https://zenkelab.org/auryn/ 2026-04-18T13:15:58+00:00 https://zenkelab.org/auryn/ https://zenkelab.org/auryn/lib/exe/fetch.php?media=wiki:logo.png text/html 2016-06-16T17:02:01+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:orchestrated_plasticity&rev=1466096521&do=diff Orchestrated Plasticity You will find the code to reproduce the key results of Zenke, F., Agnes, E.J., and Gerstner, W. (2015). Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nat Commun 6. on <https://github.com/fzenke/pub2015orchestrated> Getting the source To run the orchestrated plasticity simulation as described in Figure 3 of the paper download or clone the pub2015orchestrated repository. Then cd into text/html 2014-03-20T09:04:49+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:sim_background&rev=1395306289&do=diff sim_background This simulation is a bit more sophisticated because it comes with many adjustable settings. Therefore the code might not be as straight forward to read as this is the case in the more simple examples. It was used in Zenke et al. 2013 to study the stability of the asynchronous irregular background state in a balanced network in the presence of text/html 2016-07-05T23:40:00+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:sim_coba_benchmark&rev=1467762000&do=diff sim_coba_benchmark This is the Vogels-Abbott Benchmark (from Brette et al. 2007) network adapted from the examples included in PyNN. It implements a smaller version of the conductance based, self-sustained balanced network from (Vogels and Abbott (2005)). Running the program To run the program the network first needs priming with external Poisson noise before it can self-sustain its activity. To do that invoke the program with the following command line arguments text/html 2016-05-02T20:03:23+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:sim_dense&rev=1462219403&do=diff Example: sim_dense.cpp This simulation example is based on the network simulations published in H Lütcke, F Gerhard, F Zenke, W Gerstner, F Helmchen, 2013. Inference of neuronal network spike dynamics and topology from calcium imaging data. Frontiers in Neural Circuits 7. text/html 2014-10-29T01:12:48+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:sim_epsp_stp&rev=1414545168&do=diff Short term plasticity example sim_epsp_stp This simulation implements a variation of sim_epsp. A single presynaptic Poisson neuron provides input through a STPConnection which implements a Connection which implements the Tsodyks Markram synapse model [1]. The file records the presynaptic spiketrain, as well as the postsynaptic excitatory conductances and membrane voltage. text/html 2016-08-11T07:37:15+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:sim_epsp&rev=1470901035&do=diff sim_epsp This simulation sets up an excitatory and an inhibitory Poisson neuron and connects them to a single postsynaptic cell (IFGroup). It records and outputs the membrane potential in the file out_epsp.mem. Running the program The simulation can be invoked without any parameters. It creates the following command line output text/html 2015-06-09T04:24:37+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:sim_isp_big&rev=1433823877&do=diff sim_isp_big This implements an up-scaled version of sim_isp_orig which simply contains more neurons (200k excitatory neurons) and where excitatory weights have been scaled down accordingly. I wrote this network to show how graded activity patterns can be stored in the synaptic weight matrix and how these patterns can be stabilized by inhibitory plasticity. Watch this text/html 2014-01-20T10:19:59+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:sim_isp_orig&rev=1390213199&do=diff sim_isp_orig This simulation is the adapted from the network used to generate Figure 4 in [1]. It simulates a balanced network of 10,000 sparsely connected integrate-and-fire neurons[2] and features plastic inhibitory-exciatory synapses. [1] Vogels, T.P., Abbott, L.F., 2005. Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25, 10786. text/html 2016-02-04T17:39:55+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:sim_poisson&rev=1454607595&do=diff sim_poisson example Simulates an array of Poisson processes to illustrate the use of PoissonGroup. The output is written to a ras file in the same directory. Full source code: <https://github.com/fzenke/auryn/blob/master/examples/sim_poisson.cpp> Running code Running the example program sim_poisson yields an output like this one text/html 2016-07-20T20:09:36+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:sim_step_current&rev=1469045376&do=diff Step current to Izhikevich neuron This example can be invoked by running sim_step_current. It runs a short simulation that injects a step current to an Izhikevich neuron (see IzhikevichGroup). It writes the files step_current.0.ras with the spike times and step_current.0.mem text/html 2018-06-03T13:05:51+00:00 Anonymous (anonymous@undisclosed.example.com) https://zenkelab.org/auryn/doku.php?id=examples:start&rev=1528031151&do=diff Examples To get started you should have a look at a few examples written with Auryn. The following simulations come with Auryn when downloaded and can be found in the ./examples folder under the Auryn root directory. Starting from Auryn v0.7.0, examples are compiled automatically when building the simulator. See