Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

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examples:start [2017/11/23 09:52] – Changes structure zenkeexamples:start [2018/06/03 13:05] (current) – Updates reference zenke
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   * [[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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 The code for these works can be found in separate repositories, but in some cases 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.
  
-  * Zenke, F., and Ganguli, S. (2017). SuperSpike: Supervised learning in multi-layer spiking neural networks. ArXiv:1705.11146 [Cs, q-Bio, Stat]. [[https://arxiv.org/abs/1705.11146]]+  * 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]]   * 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., Augustine, C., Paul, S., and Detorakis, G. (2016). Neuromorphic Deep Learning Machines. arXiv:1612.05596 [Cs]. [[https://arxiv.org/abs/1612.05596]]
examples/start.txt · Last modified: 2018/06/03 13:05 by zenke