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)

Simulation module