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.
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")
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
a = analyze_raster(sd)
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