tutorials:tutorial_3
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tutorials:tutorial_3 [2016/09/01 22:32] – [Tutorial 3: Balanced network with synaptic plasticity (triplet STDP)] zenke | tutorials:tutorial_3 [2016/09/02 04:45] – section structure zenke | ||
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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:// | + | 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:// |
The code of this example can be found here | The code of this example can be found here | ||
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That's it. Save the file and [[manual: | That's it. Save the file and [[manual: | ||
- | ===== Visualizations ===== | + | |
+ | ====== Visualizations | ||
As you can see the transition around 10s where we switch plasticity on is quite drastic: | As you can see the transition around 10s where we switch plasticity on is quite drastic: | ||
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- | ===== Speeding up simulations: | + | ====== Speeding up simulations: |
By now you have simulated plastic recurrent spiking neural networks with Auryn. You might have noticed that these simulations have started to be increasingly time consuming. Fortunately, | By now you have simulated plastic recurrent spiking neural networks with Auryn. You might have noticed that these simulations have started to be increasingly time consuming. Fortunately, | ||
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- | ===== Exercises ===== | + | ====== Exercises |
* Speed up the simulation using parallelism | * Speed up the simulation using parallelism | ||
* Find a slower tau for which the network activity oscillates or explodes (see for [[http:// | * Find a slower tau for which the network activity oscillates or explodes (see for [[http:// | ||
* Add inhibitory synaptic plasticity to this simulation (see [[manual: | * Add inhibitory synaptic plasticity to this simulation (see [[manual: |
tutorials/tutorial_3.txt · Last modified: 2016/09/02 04:46 by zenke