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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¶
References¶
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.
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(1)
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.
nest.Simulate(50.0)
voltage_trace.from_device(vm)
plt.show()