Table of Contents
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.
Example code included with Auryn
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.
Here a few more common network simulation examples.
- sim_coba_benchmark The Vogels and Abbott network  in its 4000 neuron conductance based synapses version as used in [7,8].
- sim_background A simulation implementing homeostatic triplet STDP at excitatory synapses. It was used in .
- 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 .
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 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., 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).
 Vogels, T.P., Abbott, L.F., 2005. Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25, 10786. PubMed
 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
 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
 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
 Brunel, N., 2000. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8, 183–208. Full Text
 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
 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
 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