Using CSA for connection setup

Run this example as a Jupyter notebook:

See our guide for more information and troubleshooting.

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].

See Also

Using CSA with spatial populations


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

import matplotlib.pyplot as plt
import nest
from nest import visualization, voltage_trace

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

    import csa

    haveCSA = True
except ImportError:
        "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


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.

cg = 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 Connect function with the conngen rule. It takes the IDs of pre- and postsynaptic neurons (pre and post), the connection set (cg) and a dictionary that maps the parameters weight and delay to positions in the value set associated with the connection set (params_map).

params_map = {"weight": 0, "delay": 1}
connspec = {"rule": "conngen", "cg": cg, "params_map": params_map}
nest.Connect(pre, post, connspec)

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 postsynaptic 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 postsynaptic nodes are plotted.


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