Auryn simulator

Simulator for spiking neural networks with synaptic plasticity

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tutorials:tutorial_2 [2016/09/01 21:04] – created zenketutorials:tutorial_2 [2018/02/28 17:58] (current) – [Visualizing the spikes] Adds gnuplot code for spike raster 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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 </code> </code>
  
-Let's take a look at the spikes (in the [[manual:ras]] file first).+Let's take a look at the spikes (in the [[manual:ras]] file first). In [[gnuplot]] this can be done as follows: 
 +<code> 
 +set xrange [2:5] 
 +set yrange [:5000] 
 +plot 'exc.0.ras' with dots lc -1 
 +</code> 
 {{ :tutorials:tutorial2_exc_spikes.png?300 |}} {{ :tutorials:tutorial2_exc_spikes.png?300 |}}
 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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 After some initial burn in period of the network dynamics the neuron seems to start firing more irregularly. After some initial burn in period of the network dynamics the neuron seems to start firing more irregularly.
  
 +
 +===== Writing a cell assembly into the E-to-E connections =====
 +
 +Now for the fun of it, let's add a simple cell assembly to the network. Auryn support multiple ways of doing that, but the simples is to simply write a block into the weight matrix. Let's add the following code after our run instruction:
 +<code c++>
 +con_ee->set_block(0,500,0,500,5*weight);
 +sys->run(2);
 +</code>
 +which increases weights in a diagonal block in the E->E connections and then runs the simulation for another 2 seconds.
 +
 +Plotting again reveals the effect of this change in the spiking activity:
 +{{ :tutorials:tutorial2_exc_spikes_assembly.png?300 |}}
 +
 +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 ======
 +
 +  * Run the same simulation in parallel 
 +  * Tune the connectivity parameters to asynchronous irregular firing at lower firing rates
 +  * Add three cell assemblies and make them multi stable
  
tutorials/tutorial_2.1472763896.txt.gz · Last modified: 2016/09/01 21:04 by zenke