Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

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examples:start [2016/04/09 21:07]
zenke More reshuffling
examples:start [2018/06/03 15:05] (current)
zenke Updates reference
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 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. 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.
  
-You can build all examples by issuing ''​make examples''​ in the build directory (e.g. build/​home/;​ this should work on Ubuntu systems with release 0.4.1 or newer -- starting ​from v0.7.0 examples are compiled automatically when building the simulator). More flexible build chains are available for other systems. See for instance, ​[[manual:​CompileAuryn]] to learn how to build Auryn and its examples using ''​cmake''​ on diverese ​platforms.+Starting ​from Auryn v0.7.0examples 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.
   * [[sim_epsp]] Another rather simple simulation illustrating the recording of voltage or conductance traces from a single neuron.   * [[sim_epsp]] Another rather simple simulation illustrating the recording of voltage or conductance traces from a single neuron.
   * [[sim_epsp_stp]] A variation of the previous example, but using [[manual:​STPConnection]] which implements a synapse model with short term plasticity.   * [[sim_epsp_stp]] A variation of the previous example, but using [[manual:​STPConnection]] which implements a synapse model with short term plasticity.
 +  * [[sim_step_current]] Simulates step current input to a neuron (in this particular example to the Izhikevich model)
  
  
-======  Published work  ====== +==== Network simulations ​====
- +
-===== Example code that comes with Auryn  =====+
  
-These examples ​come with auryn in the /examples directory.+Here a few more common network simulation ​examples.
  
   * [[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.O., Pedroni, B.U., Joshi, S., Al-Shedivat,​ M., and Cauwenberghs,​ G. (2015). Unsupervised ​Learning ​in Synaptic Sampling ​Machines. [[http://arxiv.org/abs/1511.04484|arXiv:1511.04484]]. +  ​* 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., 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]]. The [[Orchestrated Plasticity]] code is publicly available at [[https://​github.com/​fzenke/​pub2015orchestrated]]. +  * 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]] 
-  * Ziegler, L., Zenke, F., Kastner, D.B., and Gerstner, W. (2015). Synaptic Consolidation:​ From Synapses to Behavioral Modeling. [[http://​www.jneurosci.org/​content/​35/​3/​1319|J Neurosci 35, 1319–1334]].+  * 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|FrontNeurosci 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]]. 
 +  * Ziegler, L., Zenke, F., Kastner, D.B., and Gerstner, W. (2015). Synaptic Consolidation:​ From Synapses to Behavioral Modeling. [[http://​www.jneurosci.org/​content/​35/​3/​1319|J Neurosci 35, 1319–1334]]. [[https://​github.com/​idiot-z/​zynapse|simulation code]] 
 +  * Zenke, F., and Gerstner, W. (2014). Limits to high-speed simulations of spiking neural networks using general-purpose computers. [[http://​journal.frontiersin.org/​article/​10.3389/​fninf.2014.00076/​abstract|Front Neuroinform 8, 76.]] (simulation code included in Auryn). 
 +  * Zenke, F., Hennequin, G., and Gerstner, W. (2013). Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector. [[http://​journals.plos.org/​ploscompbiol/​article?​id=10.1371/​journal.pcbi.1003330|PLoS Comput Biol 9, e1003330]]. (simulation code included in Auryn). 
 +  * Vogels, T.P., Sprekeler, H., Zenke, F., Clopath, C., and Gerstner, W. (2011). Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. [[http://​science.sciencemag.org/​content/​334/​6062/​1569|Science 334, 1569–1573]]. (simulation code included in Auryn).
  
  
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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.1460228851.txt.gz · Last modified: 2016/04/09 21:07 by zenke