Using CSA for connection setup

This example sets up a simple network in NEST using the Connection Set Algebra (CSA) instead of using the built-in connection routines.

Using the CSA requires NEST to be compiled with support for libneurosim. For details, see 1.

References

1

Djurfeldt M, Davison AP and Eppler JM (2014). Efficient generation of connectivity in neuronal networks from simulator-independent descriptions, Front. Neuroinform. http://dx.doi.org/10.3389/fninf.2014.00043

First, we import all necessary modules for simulation and plotting.

import nest
from nest import voltage_trace
from nest import visualization

Next, we check for the availability of the CSA Python module. If it does not import, we exit with an error message.

try:
    import csa
    haveCSA = True
except ImportError:
    print("This example requires CSA to be installed in order to run.\n" +
          "Please make sure you compiled NEST using\n" +
          "  -Dwith-libneurosim=[OFF|ON|</path/to/libneurosim>]\n" +
          "and CSA and libneurosim are available.")
    import sys
    sys.exit()

To set up the connectivity, We create a random connection set with a probability of 0.1 and two associated values (10000.0 and 1.0) used as weight and delay, respectively.

cs = csa.cset(csa.random(0.1), 10000.0, 1.0)

Using the Create command from PyNEST, we create the neurons of the pre- and postsynaptic populations, each of which containing 16 neurons.

pre = nest.Create("iaf_psc_alpha", 16)
post = nest.Create("iaf_psc_alpha", 16)

We can now connect the populations using the CGConnect function. It takes the IDs of pre- and postsynaptic neurons (pre and post), the connection set (cs) and a dictionary that maps the parameters weight and delay to positions in the value set associated with the connection set.

nest.CGConnect(pre, post, cs, {"weight": 0, "delay": 1})

To stimulate the network, we create a poisson_generator and set it up to fire with a rate of 100000 spikes per second. It is connected to the neurons of the pre-synaptic population.

pg = nest.Create("poisson_generator", params={"rate": 100000.0})
nest.Connect(pg, pre, "all_to_all")

To measure and record the membrane potentials of the neurons, we create a voltmeter and connect it to all post-synaptic nodes.

vm = nest.Create("voltmeter")
nest.Connect(vm, post, "all_to_all")

We save the whole connection graph of the network as a PNG image using the plot_network function of the visualization submodule of PyNEST.

allnodes = pg + pre + post + vm
visualization.plot_network(allnodes, "csa_example_graph.png")

Finally, we simulate the network for 50 ms. The voltage traces of the post-synaptic nodes are plotted.

nest.Simulate(50.0)
voltage_trace.from_device(vm)

Total running time of the script: ( 0 minutes 0.000 seconds)

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