Simulation module¶

Content¶

nngt.simulation.activity_types(spike_detector, limits, network=None, phase_coeff=(0.5, 10.0), mbis=0.5, mfb=0.2, mflb=0.05, skip_bursts=0, simplify=False, fignums=[], show=False)[source]¶

Analyze the spiking pattern of a neural network.

@todo:

think about inserting t=0. and t=simtime at the beginning and at the end of times.

Parameters:

spike_detector (NEST node(s), (tuple or list of tuples)) – The recording device that monitored the network’s spikes limits (tuple of floats) – Time limits of the simulation regrion which should be studied (in ms). network (Network, optional (default: None)) – Neural network that was analyzed phase_coeff (tuple of floats, optional (default: (0.2, 5.))) – A phase is considered bursting’ when the interspike between all spikes that compose it is smaller than ``phase_coeff[0] / avg_rate` (where avg_rate is the average firing rate), quiescent’ when it is greater that ``phase_coeff[1] / avg_rate`, `mixed’ otherwise. mbis (float, optional (default: 0.5)) – Maximum interspike interval allowed for two spikes to be considered in the same burst (in ms). mfb (float, optional (default: 0.2)) – Minimal fraction of the neurons that should participate for a burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as localized). mflb (float, optional (default: 0.05)) – Minimal fraction of the neurons that should participate for a local burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as mixed). skip_bursts (int, optional (default: 0)) – Skip the skip_bursts first bursts to consider only the permanent regime. simplify (bool, optional (default: False)) – If True, mixed phases that are contiguous to a burst are incorporated to it. return_steps (bool, optional (default: False)) – If True, a second dictionary, phases_steps will also be returned. @todo: not implemented yet fignums (list, optional (default: [])) – Indices of figures on which the periods can be drawn. show (bool, optional (default: False)) – Whether the figures should be displayed.

Note

Effects of skip_bursts and limits[0] are cumulative: the limits[0] first milliseconds are ignored, then the skip_bursts first bursts of the remaining activity are ignored.

Returns:	phases (dict) – Dictionary containing the time intervals (in ms) for all four phases (bursting’, `quiescent’, `mixed’, and `localized) as lists. E.g: phases["bursting"] could give [[123.5,334.2], [857.1,1000.6]].

nngt.simulation.get_nest_network(id_converter=None)[source]¶

Get the adjacency matrix describing a NEST network.

Parameters:	id_converter (dict, optional (default: None)) – A dictionary which maps NEST gids to the desired neurons ids.
Returns:	mat_adj (lil_matrix) – Adjacency matrix of the network.

nngt.simulation.make_nest_network(network, use_weights=True)[source]¶

Create a new network which will be filled with neurons and connector objects to reproduce the topology from the initial network.

Parameters:	network (nngt.Network or nngt.SpatialNetwork) – the network we want to reproduce in NEST. use_weights (bool, optional (default: True)) – Whether to use the network weights or default ones (value: 10.).
Returns:	gids (tuple (nodes in NEST)) – GIDs of the neurons in the network.

nngt.simulation.monitor_groups(group_names, network, nest_recorder=None, params=None)[source]¶

Monitoring the activity of nodes in the network.

Parameters:

group_name (list of strings) – Names of the groups that should be recorded. network (Network or subclass) – Network which population will be used to differentiate groups. nest_recorder (strings or list, optional (default: “spike_detector”0)) – Device(s) to monitor the network. params (dict or list of, optional (default: {})) – Dictionarie(s) containing the parameters for each recorder (see NEST documentation for details).

Returns:

recorders (tuple) – Tuple of the recorders’ gids recordables (tuple) – Typle of the recordables’ names.

nngt.simulation.monitor_nodes(gids, nest_recorder=None, params=None, network=None)[source]¶

Monitoring the activity of nodes in the network.

Parameters:

gids (tuple of ints or list of tuples) – GIDs of the neurons in the NEST subnetwork; either one list per recorder if they should monitor different neurons or a unique list which will be monitored by all devices. nest_recorder (strings or list, optional (default: “spike_detector”)) – Device(s) to monitor the network. params (dict or list of, optional (default: {})) – Dictionarie(s) containing the parameters for each recorder (see NEST documentation for details). network (Network or subclass, optional (default: None)) – Network which population will be used to differentiate groups.

Returns:

recorders (tuple) – Tuple of the recorders’ gids recordables (tuple) – Typle of the recordables’ names.

nngt.simulation.set_noise(gids, mean, std)[source]¶

Submit neurons to a current white noise. @todo: check how NEST handles the \(\sqrt{t}\) in the standard dev.

Parameters:	gids (tuple) – NEST gids of the target neurons. mean (float) – Mean current value. std (float) – Standard deviation of the current
Returns:	noise (tuple) – The NEST gid of the noise_generator.

nngt.simulation.set_poisson_input(gids, rate)[source]¶

Submit neurons to a Poissonian rate of spikes.

Parameters:	gids (tuple) – NEST gids of the target neurons. rate (float) – Rate of the spike train.
Returns:	poisson_input (tuple) – The NEST gid of the poisson_generator.

nngt.simulation.set_step_currents(gids, times, currents)[source]¶

Set step-current excitations

Parameters:	gids (tuple) – NEST gids of the target neurons. times (list or numpy.ndarray) – List of the times where the current will change (by default the current generator is initiated at I=0. for t=0.) currents (list or numpy.ndarray) – List of the new current value after the associated time value in times.
Returns:	noise (tuple) – The NEST gid of the noise_generator.

nngt.simulation.plot_activity(gid_recorder=None, record=None, network=None, gids=None, show=True, limits=None, hist=True, title=None, fignum=None, label=None, sort=None, normalize=1.0, decimate=None)[source]¶

Plot the monitored activity.

Parameters:

gid_recorder (tuple or list, optional (default: None)) – The gids of the recording devices. If None, then all existing “spike_detector”s are used. record (tuple or list, optional (default: None)) – List of the monitored variables for each device. If gid_recorder is None, record can also be None and only spikes are considered. network (Network or subclass, optional (default: None)) – Network which activity will be monitored. gids (tuple, optional (default: None)) – NEST gids of the neurons which should be monitored. show (bool, optional (default: True)) – Whether to show the plot right away or to wait for the next plt.show(). hist (bool, optional (default: True)) – Whether to display the histogram when plotting spikes rasters. limits (tuple, optional (default: None)) – Time limits of the plot (if not specified, times of first and last spike for raster plots). title (str, optional (default: None)) – Title of the plot. fignum (int, optional (default: None)) – Plot the activity on an existing figure (from figure.number). label (str, optional (default: None)) – Add a label to the plot. sort (str or list, optional (default: None)) – Sort neurons using a topological property (“in-degree”, “out-degree”, “total-degree” or “betweenness”), an activity-related property (“firing_rate”) or a user-defined list of sorted neuron ids. Sorting is performed by increasing value of the sort property from bottom to top inside each group. normalize (float, optional (default: None)) – Normalize the recorded results by a given float. decimate (int or list of ints, optional (default: None)) – Represent only a fraction of the spiking neurons; only one neuron in decimate will be represented (e.g. setting decimate to 5 will lead to only 20% of the neurons being represented). If a list is provided, it must have one entry per NeuralGroup in the population.

Warning

Sorting with “firing_rate” only works if NEST gids form a continuous integer range.

Returns:	fignums (list) – List of the figure numbers.

nngt.simulation.raster_plot(times, senders, limits=None, title='Spike raster', hist=False, num_bins=1000, color='b', decimate=None, fignum=None, label=None, show=True, sort=None, sort_attribute=None)[source]¶

Plotting routine that constructs a raster plot along with an optional histogram.

Parameters:

times (list or numpy.ndarray) – Spike times. senders (list or numpy.ndarray) – Index for the spiking neuron for each time in times. limits (tuple, optional (default: None)) – Time limits of the plot (if not specified, times of first and last spike). title (string, optional (default: ‘Spike raster’)) – Title of the raster plot. hist (bool, optional (default: True)) – Whether to plot the raster’s histogram. num_bins (int, optional (default: 1000)) – Number of bins for the histogram. color (string or float, optional (default: ‘b’)) – Color of the plot lines and markers. decimate (int, optional (default: None)) – Represent only a fraction of the spiking neurons; only one neuron in decimate will be represented (e.g. setting decimate to 10 will lead to only 10% of the neurons being represented). fignum (int, optional (default: None)) – Id of another raster plot to which the new data should be added. label (str, optional (default: None)) – Label the current data. show (bool, optional (default: True)) – Whether to show the plot right away or to wait for the next plt.show().

Returns:

fig.number (int) – Id of the matplotlib.Figure on which the raster is plotted.