Auryn simulator

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.
====== 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 [[manual:CompileAuryn]] to learn how to build Auryn and its examples using ''cmake'' on diverse platforms.

===== Example code included with Auryn  =====

The following examples can be found Auryn's /examples directory.

==== 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_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_step_current]] Simulates step current input to a neuron (in this particular example to the Izhikevich model)


==== Network simulations ====

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_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_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_brunel2k]] and [[sim_brunel2k_pl]] Adapted from the Brunel balanced network [5] following the lines of [6] with and without STDP. We used these simulations for comparison with NEST in [8].


=====  Published work using Auryn  =====

The code for these works can be found in separate repositories, but in some cases it might be closed too.

  * 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]]
  * 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]]. 
  * 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).




 

===== 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.
[[http://www.ncbi.nlm.nih.gov/pubmed/16291952|PubMed]]

[2] Vogels, T.P., Sprekeler, H., Zenke, F., Clopath, C., Gerstner, W., 2011. Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. Science 334, 1569 –1573.
[[http://www.ncbi.nlm.nih.gov/pubmed/22075724|PubMed]]

[3] Zenke, F., Hennequin, G., Gerstner, W., 2013. Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector. PLoS Comput Biol 9, e1003330.
[[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003330|Full Text]]

[4] 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.
[[http://www.frontiersin.org/Journal/10.3389/fncir.2013.00201/abstract|Full Text]]

[5] Brunel, N., 2000. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8, 183–208. [[http://link.springer.com/article/10.1023/A:1008925309027|Full Text]]

[6] Gewaltig, M.-O., Morrison, A., Plesser, H.E., 2012. NEST by Example: An Introduction to the Neural Simulation Tool NEST, in: Le Novère, N. (Ed.), Computational Systems Neurobiology. Springer Netherlands, pp. 533–558. [[http://www.scholarpedia.org/article/NEST_%28NEural_Simulation_Tool%29|Full Text]]

[7] Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J., Diesmann, M., Morrison, A., Goodman, P., Harris, F., et al. (2007). Simulation of networks of spiking neurons: A review of tools and strategies. Front Comput Neurosci 23, 349–398. [[http://link.springer.com/article/10.1007%2Fs10827-007-0038-6|Full Text]]

[8] Zenke, F. and Gerstner, W., 2014. Limits to high-speed simulations of spiking neural networks using general-purpose computers. Front Neuroinform 8, 76. doi: [[http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00076|Full Text]]