Simulation module

Module to interact easily with the NEST simulator. It allows to :

  • build a NEST network from Network or SpatialNetwork objects,
  • monitor the activity of the network (taking neural groups into account)
  • plot the activity while separating the behaviours of predefined neural groups

Content

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

Create a new subnetwork 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:

  • subnet (tuple (node in NEST)) – GID of the new NEST subnetwork
  • gids (tuple (nodes in NEST)) – GIDs of the neurons in subnet
nngt.simulation.get_nest_network(nest_subnet, id_converter=None)[source]

Get the adjacency matrix describing a NEST subnetwork.

Parameters:
  • nest_subnet (tuple) – Subnetwork node in NEST.
  • 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.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.monitor_nodes(gids, nest_recorder=['spike_detector'], params=[{}], 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 (list of strings, optional (default: [“spike_detector”])) – List of devices to monitor the network.
  • params (list of dict, optional (default: [{}])) – List of dictionaries containing the parameters for each recorder (see NEST documentation for details).
  • network (Network or subclass, optional (default: “”)) – Network which population will be used to differentiate inhibitory and excitatory spikes.
Returns:

recorders (tuple) – Tuple of the recorders’ gids
nngt.simulation.plot_activity(gid_recorder, record, network=None, gids=None, show=True, limits=None, hist=True)[source]

Plot the monitored activity.

Parameters:
  • gid_recorder (tuple or list) – The gids of the recording devices.
  • record (tuple or list) – List of the monitored variables for each device.
  • 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).
Returns:

fignums (list) – List of the figure numbers.
nngt.simulation.activity_types(network, spike_detector, limits, phase_coeff=(0.5, 10.0), mbis=0.5, mfb=0.2, mflb=0.05, simplify=False, fignums=[], show=False)[source]

Analyze the spiking pattern of a neural network. @todo: think about inserting t= and t=simtime at the beginning and at the end of ``times’‘.

Parameters:
  • network (Network) – Neural network that was analyzed
  • 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).
  • 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).
  • 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.
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]].
  • phases_steps (dict, optional (not implemented yet)) – Dictionary containing the timesteps in NEST.