PyNEST API listing¶

Connection module ¶

Functions for connection handling

`Connect`	Connect pre nodes to post nodes.
`TripartiteConnect`	Connect pre nodes to post nodes and a third-factor nodes.
`Disconnect`	Disconnect connections in a SynapseCollection, or pre neurons from post neurons.
`GetConnections`	Return a SynapseCollection representing the connection identifiers.

Info module ¶

Functions to get information on NEST.

`authors`	Print the authors of NEST.
`get_argv`	Return argv as seen by NEST.
`GetStatus`	Return the parameter dictionaries of nodes or connections.
`get_verbosity`	Return verbosity level of NEST's messages.
`help`	Display the help page for the given object in a pager.
`helpdesk`	Open the NEST documentation index in a browser.
`message`	Print a message using message system of NEST.
`SetStatus`	Set parameters of nodes or connections.
`set_verbosity`	Change verbosity level for NEST's messages.
`sysinfo`	Print information on the platform on which NEST was compiled.

Models module ¶

Functions for model handling

`ConnectionRules`	Return a tuple of all available connection rules, sorted by name.
`CopyModel`	Create a new model by copying an existing one.
`GetDefaults`	Return defaults of the given model or recording backend.
`Models`	Return a tuple of neuron, device, or synapse model names.
`SetDefaults`	Set defaults for the given model or recording backend.

Node module ¶

Functions for node handling

`Create`	Create one or more nodes.
`GetLocalNodeCollection`	Get local nodes of a NodeCollection as a new NodeCollection.
`GetNodes`([properties, local_only])	Return all nodes with the given properties as NodeCollection.
`PrintNodes`	Print the node ID ranges and model names of all the nodes in the network.

Parallel computing module ¶

Functions for parallel computing

`NumProcesses`	Return the overall number of MPI processes.
`Rank`	Return the MPI rank of the local process.
`GetLocalVPs`	Return iterable representing the VPs local to the MPI rank.
`SetAcceptableLatency`	Set the acceptable latency (in ms) for a MUSIC port.
`SetMaxBuffered`	Set the maximum buffer size for a MUSIC port.
`SyncProcesses`	Synchronize all MPI processes.

Simulation module ¶

Functions for simulation control

`Cleanup`	Cleans up resources after a Run calls.
`DisableStructuralPlasticity`	Disable structural plasticity for the network simulation
`EnableStructuralPlasticity`	Enable structural plasticity for the network simulation
`GetKernelStatus`	Obtain parameters of the simulation kernel.
`Install`	Load a dynamically linked NEST module.
`Prepare`	Calibrate the system before a Run call.
`ResetKernel`	Reset the simulation kernel.
`Run`	Simulate the network for t milliseconds.
`RunManager`()	ContextManager for Run
`SetKernelStatus`	Set parameters for the simulation kernel.
`Simulate`	Simulate the network for t milliseconds.

Sonata module ¶

Functions to build and simulate networks with the SONATA format

SonataNetwork(config[, sim_config])

Class for building and simulating networks represented by the SONATA format.

Spatial module ¶

Functions relating to spatial properties of nodes

`CreateMask`(masktype, specs[, anchor])	Create a spatial mask for connections.
`Displacement`(from_arg, to_arg)	Get vector of lateral displacement from node(s)/Position(s) from_arg to node(s) to_arg.
`Distance`(from_arg, to_arg)	Get lateral distances from node(s)/position(s) from_arg to node(s) to_arg.
`DumpLayerConnections`(source_layer, ...)	Write connectivity information to file.
`DumpLayerNodes`(layer, outname)	Write node ID and position data of layer to file.
`FindCenterElement`(layer)	Return NodeCollection of node closest to center of layer.
`FindNearestElement`(layer, locations[, find_all])	Return the node(s) closest to the locations in the given layer.
`GetPosition`(nodes)	Return the spatial locations of nodes.
`GetTargetNodes`(sources, tgt_layer[, syn_model])	Obtain targets of sources in given target population.
`GetSourceNodes`(src_layer, targets[, syn_model])	Obtain sources of targets in given src_layer population.
`GetTargetPositions`(sources, tgt_layer[, ...])	Obtain positions of targets to a given NodeCollection of sources.
`GetSourcePositions`(src_layer, targets[, ...])	Obtain positions of sources to a given NodeCollection of targets.
`PlotLayer`(layer[, fig, nodecolor, nodesize])	Plot all nodes in a layer.
`PlotProbabilityParameter`(source[, ...])	Create a plot of the connection probability and/or mask.
`PlotTargets`(src_nrn, tgt_layer[, syn_type, ...])	Plot all targets of source neuron src_nrn in a target layer tgt_layer.
`PlotSources`(src_layer, tgt_nrn[, syn_type, ...])	Plot all sources of target neuron tgt_nrn in a source layer src_layer.
`SelectNodesByMask`(layer, anchor, mask_obj)	Obtain the node IDs inside a masked area of a spatially distributed population.

Types module ¶

Classes defining the different PyNEST types

`CollocatedSynapses`(*args)	Class for collocated synapse specifications.
`Compartments`(node_collection, elements)
`CreateParameter`(parametertype, specs)	Create a parameter.
`Mask`(datum)	Class for spatial masks.
`NodeCollection`([data])	Class for NodeCollection.
`Parameter`(datum)	Class for parameters
`Receptors`(node_collection, elements)
`serialize_data`(data)	Serialize data for JSON.
`SynapseCollection`(data)	Class for Connections.
`to_json`(data, **kwargs)	Convert the object to a JSON string.

Logic module ¶

conditional(condition, param_if_true, ...)

Yields one value or another, based on the condition.

Math module ¶

`exp`(parameter)	Calculate the exponential of the parameter
`sin`(parameter)	Calculate the sine of the parameter
`cos`(parameter)	Calculate the cosine of the parameter
`min`(parameter, value)	Yields the smallest value of the value of a parameter and a given value
`max`(parameter, value)	Yields the largest value of the value of a parameter and a given value
`redraw`(parameter, min, max)	Redraws the value of the parameter if it is outside of the given limits

Kernel attributes (nest.NestModule)¶

A module class for the nest root module to control the dynamic generation of module level attributes such as the KernelAttributes, lazy loading some submodules and importing the public APIs of the lib submodules.

`kernel_status`	Get the complete kernel status.
`resolution`	The resolution of the simulation (in ms), defaults to `0.1`.
`biological_time`	The current simulation time (in ms).
`to_do`	The number of steps yet to be simulated.
`max_delay`	The maximum delay in the network, defaults to `0.1`.
`min_delay`	The minimum delay in the network, defaults to `0.1`.
`ms_per_tic`	The number of milliseconds per tic.
`tics_per_ms`	The number of tics per millisecond.
`tics_per_step`	The number of tics per simulation time step.
`T_max`	The largest representable time value.
`T_min`	The smallest representable time value.
`rng_types`	List of available random number generator types.
`rng_type`	Name of random number generator type used by NEST, defaults to `mt19937_64`.
`rng_seed`	Seed value used as base for seeding NEST random number generators (\(1 \leq s\leq 2^{32}-1\)), defaults to `143202461`.
`total_num_virtual_procs`	The total number of virtual processes, defaults to `1`.
`local_num_threads`	The local number of threads, defaults to `1`.
`num_processes`	The number of MPI processes.
`off_grid_spiking`	Whether to transmit precise spike times in MPI communication.
`adaptive_target_buffers`	Whether MPI buffers for communication of connections resize on the fly, defaults to `True`.
`send_buffer_size_secondary_events`	Size of MPI send buffers for communicating secondary events (in bytes, per MPI rank, for developers).
`recv_buffer_size_secondary_events`	Size of MPI recv buffers for communicating secondary events (in bytes, per MPI rank, for developers).
`buffer_size_spike_data`	Total size of MPI buffer for communication of spikes, defaults to `2`.
`buffer_size_target_data`	Total size of MPI buffer for communication of connections, defaults to `2`.
`growth_factor_buffer_target_data`	If MPI buffers for communication of connections resize on the fly, grow them by this factor each round, defaults to `1.5`.
`max_buffer_size_target_data`	Maximal size of MPI buffers for communication of connections, defaults to `16777216`.
`spike_buffer_grow_extra`	When spike exchange buffer is expanded, resize it to (1 + spike_buffer_grow_extra) * required_buffer_size, defaults to `0.5`.
`spike_buffer_shrink_limit`	If the largest number of spikes sent from any rank to any rank is less than spike_buffer_shrink_limit * buffer_size, then reduce buffer size.
`spike_buffer_shrink_spare`	When the buffer shrinks, set the new size to (1 + spike_buffer_shrink_spare) * required_buffer_size.
`spike_buffer_resize_log`	Log of spike buffer resizing as a dictionary.
`use_wfr`	Whether to use waveform relaxation method, defaults to `True`.
`wfr_comm_interval`	Desired waveform relaxation communication interval, defaults to `1.0`.
`wfr_tol`	Convergence tolerance of waveform relaxation method, defaults to `0.0001`.
`wfr_max_iterations`	Maximal number of iterations used for waveform relaxation, defaults to `15`.
`wfr_interpolation_order`	Interpolation order of polynomial used in wfr iterations, defaults to `3`.
`max_num_syn_models`	Maximal number of synapse models supported.
`structural_plasticity_synapses`	Defines all synapses which are plastic for the structural plasticity algorithm.
`structural_plasticity_update_interval`	Defines the time interval in ms at which the structural plasticity manager will make changes in the structure of the network ( creation and deletion of plastic synapses), defaults to `10000`.
`growth_curves`	The list of the available structural plasticity growth curves.
`use_compressed_spikes`	Whether to use spike compression; if a neuron has targets on multiple threads of a process, this switch makes sure that only a single packet is sent to the process instead of one packet per target thread; it implies that connections are sorted by source., defaults to `True`.
`data_path`	A path, where all data is written to, defaults to current directory.
`data_prefix`	A common prefix for all data files.
`overwrite_files`	Whether to overwrite existing data files, defaults to `False`.
`print_time`	Whether to print progress information during the simulation, defaults to `False`.
`network_size`	The number of nodes in the network.
`num_connections`	The number of connections in the network.
`connection_rules`	The list of available connection rules.
`node_models`	The list of the available node (i.e., neuron or device) models.
`synapse_models`	The list of the available synapse models.
`local_spike_counter`	Number of spikes fired by neurons on a given MPI rank during the most recent call to `Simulate()`.
`recording_backends`	List of available backends for recording devices.
`stimulation_backends`	List of available backends for stimulation devices.
`dict_miss_is_error`	Whether missed dictionary entries are treated as errors, defaults to `True`.
`keep_source_table`	Whether to keep source table after connection setup is complete, defaults to `True`.
`min_update_time`	Shortest wall-clock time measured so far for a full update step [seconds].
`max_update_time`	Longest wall-clock time measured so far for a full update step [seconds].
`update_time_limit`	Maximum wall-clock time for one full update step [seconds].
`eprop_update_interval`	Task-specific update interval of the e-prop plasticity mechanism [ms]., defaults to `1000.0`.
`eprop_learning_window`	Task-specific learning window of the e-prop plasticity mechanism [ms]., defaults to `1000.0`.
`eprop_reset_neurons_on_update`	If True, reset dynamic variables of e-prop neurons upon e-prop update., defaults to `True`.

Random module ¶

`exponential`([beta])	Draws samples from an exponential distribution.
`lognormal`([mean, std])	Draws samples from a log-normal distribution.
`normal`([mean, std])	Draws samples from a normal distribution.
`uniform`([min, max])	Draws samples from a uniform distribution.
`uniform_int`(max)	Draws integer samples from a uniform distribution.

Raster_plot module ¶

Functions for raster plotting.

`extract_events`(data[, time, sel])	Extract all events within a given time interval.
`from_data`(data[, sel])	Plot raster plot from data array.
`from_device`(detec, **kwargs)	Plot raster from a spike recorder.
`from_file`(fname, **kwargs)	Plot raster from file.
`from_file_numpy`(fname, **kwargs)	Use numpy.
`from_file_pandas`(fname, **kwargs)	Use pandas.

Server module ¶

`app`	The flask object implements a WSGI application and acts as the central object.
`do_exec`(args, kwargs)
`set_mpi_comm`(comm)
`run_mpi_app`([host, port])
`nestify`(call_name, args, kwargs)	Get the NEST API call and convert arguments if neccessary.

Spatial positions functions ¶

`grid`(shape[, center, extent, edge_wrap])	Defines grid-based positions for nodes.
`free`(pos[, extent, edge_wrap, num_dimensions])	Defines positions for nodes based on a list of positions, or a Parameter object.
`pos`()	Position of node in a specific dimension.
`source_pos`()	Position of the source node in a specific dimension.
`target_pos`()	Position of the target node in a specific dimension.

Spatial distribution functions ¶

`exponential`(x[, beta])	Applies an exponential distribution on a Parameter.
`gaussian`(x[, mean, std])	Applies a gaussian distribution on a Parameter.
`gaussian2D`(x, y[, mean_x, mean_y, std_x, ...])	Applies a bivariate gaussian distribution on two Parameters, representing values in the x and y direction.
`gamma`(x[, kappa, theta])	Applies a gamma distribution on a Parameter.
`gabor`(x, y[, theta, gamma, std, lam, psi])	Applies a rectified Gabor function on two Parameters, representing displacements in the x and y direction.

Visualization module ¶

Functions to visualize a network built in NEST.

plot_network(nodes, filename[, ext_conns, ...])

Plot the given nodes and the connections that originate from them.

Voltage_trace module ¶

Functions to plot voltage traces.

`from_device`(detec[, neurons, title, ...])	Plot the membrane potential of a set of neurons recorded by the given voltmeter or multimeter.
`from_file`(fname[, title, grayscale])	Plot voltage trace from file.