Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

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examples:start [2017/02/08 23:08] – [Published work using Auryn] Adds reference zenkeexamples:start [2018/06/03 13:05] (current) – Updates reference zenke
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 Starting from Auryn v0.7.0, examples are compiled automatically when building the simulator. See [[manual:CompileAuryn]] to learn how to build Auryn and its examples using ''cmake'' on diverse platforms. Starting from Auryn v0.7.0, examples are compiled automatically when building the simulator. See [[manual:CompileAuryn]] to learn how to build Auryn and its examples using ''cmake'' on diverse platforms.
  
 +===== Example code included with Auryn  =====
  
-==== Simple examples --- First Steps ====+The following examples can be found Auryn's /examples directory.
  
-These are very simple network models which can be easily understood and modified to get a first impression of how Auryn simulations are built.+==== Basic examples ==== 
 + 
 +These are very simple models with a single neuron which can be easily understood and modified to get a first impression of how Auryn simulations are built.
  
   * [[sim_poisson]] This example is //Hello world// in Auryn. It shows you how to create a simple [[manual:PoissonGroup]] that fires at a given rate and writes the output to a [[manual:ras]] file.   * [[sim_poisson]] This example is //Hello world// in Auryn. It shows you how to create a simple [[manual:PoissonGroup]] that fires at a given rate and writes the output to a [[manual:ras]] file.
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-=====  Published work  =====+==== Network simulations ====
  
-==== Example code that comes with Auryn  ==== +Here a few more common network simulation examples.
- +
-These examples come with auryn in the /examples directory.+
  
   * [[sim_coba_benchmark]] The Vogels and Abbott network [1] in its 4000 neuron conductance based synapses version as used in [7,8].   * [[sim_coba_benchmark]] The Vogels and Abbott network [1] in its 4000 neuron conductance based synapses version as used in [7,8].
-  * [[sim_isp_orig]] This simulation illustrates inhibitory plasticity in the Vogels and Abbott network. It is the parallelized version of our network used in Figure 4 in [2]. +  * [[sim_isp_orig]] This simulation illustrates inhibitory plasticity in the Vogels and Abbott network. It is the parallelized version of our network used in Figure 4 in [2]. ([[sim_isp_big]] An up-scaled version of this network to 200,000 neurons)
-  * [[sim_isp_big]] An up-scaled version of this network to 200,000 neurons.+
   * [[sim_background]] A simulation implementing homeostatic triplet STDP at excitatory synapses. It was used in [3].   * [[sim_background]] A simulation implementing homeostatic triplet STDP at excitatory synapses. It was used in [3].
   * [[sim_dense]] simulates a 25,000 neuron network with non-plastic connectivity of 10% which receives modulated external Poisson input. Similar to what we used in [4].   * [[sim_dense]] simulates a 25,000 neuron network with non-plastic connectivity of 10% which receives modulated external Poisson input. Similar to what we used in [4].
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-==== Published work using Auryn ====+=====  Published work using Auryn  =====
  
-The code for these works can be found in separate repositories, but it might be closed too.+The code for these works can be found in separate repositories, but in some cases it might be closed too.
  
-  * Neftci, E., Augustine, C., Paul, S., and Detorakis, G. (2016). Neuromorphic Deep Learning Machines. arXiv:1612.05596 [Cs]. [[https://arxiv.org/abs/1612.05596]]+  * Zenke, F., and Ganguli, S. (2018). SuperSpike: Supervised learning in multi-layer spiking neural networks. Neural Computation 30, 1514–1541. [[https://doi.org/10.1162/neco_a_01086]] | code: https://github.com/fzenke/pub2018superspike
   * Zenke, F., and Gerstner, W. (2017). Hebbian plasticity requires compensatory processes on multiple timescales. Phil. Trans. R. Soc. B 372, 20160259. [[http://rstb.royalsocietypublishing.org/content/372/1715/20160259]]   * Zenke, F., and Gerstner, W. (2017). Hebbian plasticity requires compensatory processes on multiple timescales. Phil. Trans. R. Soc. B 372, 20160259. [[http://rstb.royalsocietypublishing.org/content/372/1715/20160259]]
 +  * Neftci, E., Augustine, C., Paul, S., and Detorakis, G. (2016). Neuromorphic Deep Learning Machines. arXiv:1612.05596 [Cs]. [[https://arxiv.org/abs/1612.05596]]
   * Neftci, E.O., Pedroni, B.U., Joshi, S., Al-Shedivat, M., and Cauwenberghs, G. (2016). Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines. [[http://dx.doi.org/10.3389/fnins.2016.00241|Front. Neurosci 241]].    * Neftci, E.O., Pedroni, B.U., Joshi, S., Al-Shedivat, M., and Cauwenberghs, G. (2016). Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines. [[http://dx.doi.org/10.3389/fnins.2016.00241|Front. Neurosci 241]]. 
   * Zenke, F., Agnes, E.J., and Gerstner, W. (2015). Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. [[http://www.nature.com/ncomms/2015/150421/ncomms7922/full/ncomms7922.html|Nat Commun 6]]. [[https://github.com/fzenke/pub2015orchestrated|simulation code]].   * Zenke, F., Agnes, E.J., and Gerstner, W. (2015). Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. [[http://www.nature.com/ncomms/2015/150421/ncomms7922/full/ncomms7922.html|Nat Commun 6]]. [[https://github.com/fzenke/pub2015orchestrated|simulation code]].
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-==== Bibliography ====+===== Bibliography =====
  
 [1] Vogels, T.P., Abbott, L.F., 2005. Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25, 10786. [1] Vogels, T.P., Abbott, L.F., 2005. Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25, 10786.
examples/start.1486595335.txt.gz · Last modified: 2017/02/08 23:08 by zenke