examples:start
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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 '' | 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 '' | ||
- | You can build all examples by issuing '' | + | Starting |
+ | ===== Example code included with Auryn ===== | ||
- | ===== Simple | + | The following examples can be found Auryn' |
+ | |||
+ | ==== 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: | * [[sim_poisson]] This example is //Hello world// in Auryn. It shows you how to create a simple [[manual: | ||
* [[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: | * [[sim_epsp_stp]] A variation of the previous example, but using [[manual: | ||
+ | * [[sim_step_current]] Simulates step current input to a neuron (in this particular example to the Izhikevich model) | ||
- | ===== Published work under /examples ===== | + | ==== Network simulations |
- | These examples | + | Here a few more common network simulation |
* [[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 ====== | + | ===== Published work using Auryn ===== |
+ | |||
+ | The code for these works can be found in separate repositories, | ||
- | The code for these works can be found in separate repositories, but 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:// | ||
+ | * Zenke, F., and Gerstner, W. (2017). Hebbian plasticity requires compensatory processes on multiple timescales. Phil. Trans. R. Soc. B 372, 20160259. [[http:// | ||
+ | * Neftci, E., Augustine, C., Paul, S., and Detorakis, G. (2016). Neuromorphic Deep Learning Machines. arXiv: | ||
+ | * Neftci, E.O., Pedroni, B.U., Joshi, S., Al-Shedivat, | ||
+ | * Zenke, F., Agnes, E.J., and Gerstner, W. (2015). Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. [[http:// | ||
+ | * Ziegler, L., Zenke, F., Kastner, D.B., and Gerstner, W. (2015). Synaptic Consolidation: | ||
+ | * Zenke, F., and Gerstner, W. (2014). Limits to high-speed simulations of spiking neural networks using general-purpose computers. [[http:// | ||
+ | * Zenke, F., Hennequin, G., and Gerstner, W. (2013). Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector. [[http:// | ||
+ | * 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:// | ||
- | * Neftci, E.O., Pedroni, B.U., Joshi, S., Al-Shedivat, | ||
- | * Zenke, F., Agnes, E.J., and Gerstner, W. (2015). Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. [[http:// | ||
- | * Ziegler, L., Zenke, F., Kastner, D.B., and Gerstner, W. (2015). Synaptic Consolidation: | ||
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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.txt · Last modified: 2018/06/03 13:05 by zenke