Activity analysis#
Principle#
The interesting fact about having a link between the graph and the simulation is that you can easily analyze the activity be taking into account what you know from the graph structure.
Sorted rasters#
Rater plots can be sorted depending on some specific node property, e.g. the degree or the betweenness:
import nest
import nngt
from nngt.simulation import monitor_nodes, plot_activity
pop = nngt.NeuralPop.uniform(1000, neuron_model="aeif_psc_alpha")
net = nngt.generation.gaussian_degree(100, 20, population=pop)
nodes = net.to_nest()
recorders, recordables = monitor_nodes(nodes)
simtime = 1000.
nest.Simulate(simtime)
fignums = plot_activity(
recorders, recordables, network=net, show=True, hist=False,
limits=(0.,simtime), sort="in-degree")
Activity properties#
NNGT can also be used to analyze the general properties of a raster.
Either from a .gdf file containing the raster data
import nngt
from nngt.simulation import analyze_raster
a = analyze_raster("path/to/raster.gdf")
print(a.phases)
print(a.properties)
Or from a spike detector gid sd
:
a = analyze_raster(sd)
Additional information:
Go to other tutorials: