Activity analysis#


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.

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")

Or from a spike detector gid sd:

a = analyze_raster(sd)

Additional information:

Go to other tutorials: