Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

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tutorials:tutorial_2 [2016/09/01 21:19] – [Visualizing the spikes] zenketutorials:tutorial_2 [2017/04/24 19:15] – Changes links to fzenke.net zenke
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 Here you will learn to wire up a simple balanced network. Here you will learn to wire up a simple balanced network.
 This assumes that you already know the basics explained in [[Tutorial 1]]. This assumes that you already know the basics explained in [[Tutorial 1]].
 +
 +The code of this example can be found here
 +https://github.com/fzenke/auryn/blob/master/examples/sim_tutorial2.cpp
 +
  
 ===== Setting up neural populations ===== ===== Setting up neural populations =====
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 IFGroup * neurons_exc = new IFGroup(nb_exc_neurons); IFGroup * neurons_exc = new IFGroup(nb_exc_neurons);
 neurons_exc->set_name("exc neurons"); neurons_exc->set_name("exc neurons");
 +neurons_exc->get_state_vector("g_nmda")->set_random();
 IFGroup * neurons_inh = new IFGroup(nb_inh_neurons); IFGroup * neurons_inh = new IFGroup(nb_inh_neurons);
 neurons_inh->set_tau_mem(5e-3); neurons_inh->set_tau_mem(5e-3);
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 </code> </code>
  
-The above code snipped initializes an excitatory population (''neurons_exc'') with 20000 neurons and an inhibitory population which is one quarter in size. Here were are using [[manual:IFGroup]] one of Auryn's standard neuron models. These neurons have conductance based synapses with a fast and slow excitatory conductance. Moreover, the model has a relative refractory mechanism. Also note that we give the inhibitory neurons a membrane time constant of 5ms.+The above code snipped initializes an excitatory population (''neurons_exc'') with 20000 neurons and an inhibitory population which is one quarter in size. Here were are using [[manual:IFGroup]] one of Auryn's standard neuron models. These neurons have conductance based synapses with a fast and slow excitatory conductance. Moreover, the model has a relative refractory mechanism. Here, we initialize the NMDA conductances of the excitatory population randomly to avoid that all neurons spike synchronously initially. That's what ''set_random()'' does. Also note that we give the inhibitory neurons a membrane time constant of 5ms.
  
 As before in [[Tutorial 1]] we define Poisson input as a separate population: As before in [[Tutorial 1]] we define Poisson input as a separate population:
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 </code> </code>
  
-==== Connecting the network ====+===== Connecting the network =====
  
 Now let's connect these three populations. First the input: Now let's connect these three populations. First the input:
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 Note that we made inhibitory connections stronger by a factor of ''gamma = 4.0''. Note that we made inhibitory connections stronger by a factor of ''gamma = 4.0''.
  
-==== Set up monitors ====+===== Set up monitors =====
  
 Let's record spikes from all neurons and the membrane potential from neuron 0 in the excitatory population. Let's record spikes from all neurons and the membrane potential from neuron 0 in the excitatory population.
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-==== Running the simulation ====+===== Running the simulation =====
  
 To run the simulation for 10 seconds we simply add the run command: To run the simulation for 10 seconds we simply add the run command:
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-===== Visualizing the spikes =====+====== Visualizing the spikes ======
  
 Running above code will generate the following files: Running above code will generate the following files:
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 That's a lot of spikes, but the image suggests that there is some synchrony going on at times, but there are also phases of asynchronous firing. Let's zoom in a bit more: That's a lot of spikes, but the image suggests that there is some synchrony going on at times, but there are also phases of asynchronous firing. Let's zoom in a bit more:
 {{ :tutorials:tutorial2_exc_spikes_zoom.png?300 |}} {{ :tutorials:tutorial2_exc_spikes_zoom.png?300 |}}
 +
 +We can also analyze this a little more and plot for instance the distribution of firing rates:
 +{{ :tutorials:tutorial2_exc_rates.png?300 |}}
  
 Now let's have a look the membrane trace we recorded from one of our cells: Now let's have a look the membrane trace we recorded from one of our cells:
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 {{ :tutorials:tutorial2_exc_spikes_assembly.png?300 |}} {{ :tutorials:tutorial2_exc_spikes_assembly.png?300 |}}
  
-For more sophisticated ways of writing patterns into a connectivity matrix, check out the documentation of [[manual:SparseConnection]] and specifically the functions called ''load_patterns'' (see also http://www.fzenke.net/auryn/doxygen/current/classauryn_1_1SparseConnection.html).+For more sophisticated ways of writing patterns or synfire chain structures into a connectivity matrix, check out the documentation of [[manual:SparseConnection]] and specifically the functions called ''load_patterns'' (see also http://fzenke.net/auryn/doxygen/current/classauryn_1_1SparseConnection.html).  
 +You can also always load a weight matrix from an external file which have generated using MATLAB or Python. Auryn supports a coordinate based [[http://math.nist.gov/MatrixMarket/|matrix market]] format which allows for the seamless exchange of weight matrices with external tools (see [[manual:wmat|matrix format]]). 
 + 
 + 
 +In the next [[Tutorial 3|tutorial]] we will make this model plastic.
  
 ====== Exercises ====== ====== Exercises ======
tutorials/tutorial_2.txt · Last modified: 2018/02/28 17:58 by zenke