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 CompileAuryn to learn how to build Auryn and its examples using cmake on diverse platforms.

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

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 PoissonGroup that fires at a given rate and writes the output to a 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 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)

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].

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. 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. Nat Commun 6. simulation code.
• Ziegler, L., Zenke, F., Kastner, D.B., and Gerstner, W. (2015). Synaptic Consolidation: From Synapses to Behavioral Modeling. J Neurosci 35, 1319–1334. simulation code
• Zenke, F., and Gerstner, W. (2014). Limits to high-speed simulations of spiking neural networks using general-purpose computers. 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. 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. Science 334, 1569–1573. (simulation code included in Auryn).

[1] Vogels, T.P., Abbott, L.F., 2005. Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25, 10786. 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. 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. 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. Full Text

[5] Brunel, N., 2000. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8, 183–208. 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. 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. 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: Full Text