Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

User Tools

Site Tools


tutorials:tutorial_3

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revisionBoth sides next revision
tutorials:tutorial_3 [2016/09/01 22:32] – [Tutorial 3: Balanced network with synaptic plasticity (triplet STDP)] zenketutorials:tutorial_3 [2016/09/02 04:45] – [Tutorial 3: Balanced network with synaptic plasticity (triplet STDP)] zenke
Line 5: Line 5:
 As you have seen in the previous section the firing rate distribution is relatively wide in our random network. Moreover firing rates are pretty high. If you have tried to tune the weights such that the network exhibits a more plausible activity level, you might have noticed that this not completely trivial and the rates can in fact be quite sensitive to the synaptic weight parameters. As you have seen in the previous section the firing rate distribution is relatively wide in our random network. Moreover firing rates are pretty high. If you have tried to tune the weights such that the network exhibits a more plausible activity level, you might have noticed that this not completely trivial and the rates can in fact be quite sensitive to the synaptic weight parameters.
  
-However, we know that real synapses are plastic and a diversity of plasticity processes is at work in real neural networks which could achieve this tuning automatically. Let's now extend our previous model with one form of homeostatic plasticity. To that end we would like to exchange our sparse static connectivity of the excitatory-to-excitatory synapses in [[Tutorial 2]] to plastic synapses. Here we will use a homeostatic form of [[http://www.ncbi.nlm.nih.gov/pubmed/16988038|Triplet STDP]] with a rapid sliding threshold (see [[http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003330| this article]] for more details on why it needs to be rapid).+However, we know that real synapses are plastic and a diversity of plasticity processes is at work in real neural networks which could achieve this tuning automatically. Let's now extend our previous model with one form of homeostatic plasticity. To that end we would like to exchange our sparse static connectivity of the excitatory-to-excitatory synapses in [[Tutorial 2]] with plastic synapses. Here we will use a homeostatic form of [[http://www.ncbi.nlm.nih.gov/pubmed/16988038|Triplet STDP]] with a rapid sliding threshold (see [[http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003330| this article]] for more details on why it needs to be rapid).
  
 The code of this example can be found here The code of this example can be found here
tutorials/tutorial_3.txt · Last modified: 2016/09/02 04:46 by zenke