Network class¶

(Shortest import: from brian2 import Network)

class brian2.core.network.Network(*objs, name='network*')[source]

The main simulation controller in Brian

Network handles the running of a simulation. It contains a set of Brian objects that are added with add. The run method actually runs the simulation. The main run loop, determining which objects get called in what order is described in detail in the notes below. The objects in the Network are accesible via their names, e.g. net['neurongroup'] would return the NeuronGroup with this name.

Parameters: objs : (BrianObject, container), optional A list of objects to be added to the Network immediately, see add. name : str, optional An explicit name, if not specified gives an automatically generated name

Notes

The main run loop performs the following steps:

1. Prepare the objects if necessary, see prepare.
2. Determine the end time of the simulation as t+duration.
3. Determine which set of clocks to update. This will be the clock with the smallest value of t. If there are several with the same value, then all objects with these clocks will be updated simultaneously. Set t to the clock time.
4. If the t value of these clocks is past the end time of the simulation, stop running. If the Network.stop() method or the stop() function have been called, stop running. Set t to the end time of the simulation.
5. For each object whose clock is set to one of the clocks from the previous steps, call the update method. This method will not be called if the active flag is set to False. The order in which the objects are called is described below.
6. Increase Clock.t by Clock.dt for each of the clocks and return to step 2.

The order in which the objects are updated in step 4 is determined by the Network.schedule and the objects when and order attributes. The schedule is a list of string names. Each when attribute should be one of these strings, and the objects will be updated in the order determined by the schedule. The default schedule is ['start', 'groups', 'thresholds', 'synapses', 'resets', 'end']. In addition to the names provided in the schedule, automatic names starting with before_ and after_ can be used. That means that all objects with when=='before_start' will be updated first, then those with when=='start', when=='after_start', when=='before_groups', when=='groups' and so forth. If several objects have the same when attribute, then the order is determined by the order attribute (lower first).

Attributes

 _stored_state Stored state of objects (store/restore) objects The list of objects in the Network, should not normally be modified directly. profiling_info The time spent in executing the various CodeObjects. schedule List of when slots in the order they will be updated, can be modified. t Current simulation time in seconds (Quantity) t_ Current time as a float

Methods

 add(*objs) Add objects to the Network after_run() before_run(namespace) Prepares the Network for a run. check_dependencies() get_profiling_info(*args, **kwds) The only reason this is not directly implemented in profiling_info is to allow devices (e.g. get_states([units, format, subexpressions, ...]) Return a copy of the current state variable values of objects in the network.. remove(*objs) Remove an object or sequence of objects from a Network. restore([name, filename]) Retore the state of the network and all included objects. run(duration[, report, report_period, ...]) Runs the simulation for the given duration. scheduling_summary() Return a SchedulingSummary object, representing the scheduling information for all objects included in the network. set_states(values[, units, format, level]) Set the state variables of objects in the network. stop() Stops the network from running, this is reset the next time Network.run() is called. store([name, filename]) Store the state of the network and all included objects.

Details

_stored_state

Stored state of objects (store/restore)

objects

The list of objects in the Network, should not normally be modified directly. Note that in a MagicNetwork, this attribute only contains the objects during a run: it is filled in before_run and emptied in after_run

profiling_info

The time spent in executing the various CodeObjects.

A list of (name, time) tuples, containing the name of the CodeObject and the total execution time for simulations of this object (as a Quantity with unit second). The list is sorted descending with execution time.

Profiling has to be activated using the profile keyword in run() or Network.run().

schedule

List of when slots in the order they will be updated, can be modified.

See notes on scheduling in Network. Note that additional when slots can be added, but the schedule should contain at least all of the names in the default schedule: ['start', 'groups', 'thresholds', 'synapses', 'resets', 'end'].

The schedule can also be set to None, resetting it to the default schedule set by the core.network.default_schedule preference.

t

Current simulation time in seconds (Quantity)

t_

Current time as a float

add(*objs)[source]

Add objects to the Network

Parameters: objs : (BrianObject, container) The BrianObject or container of Brian objects to be added. Specify multiple objects, or lists (or other containers) of objects. Containers will be added recursively. If the container is a dict then it will add the values from the dictionary but not the keys. If you want to add the keys, do add(objs.keys()).
after_run()
before_run(namespace)

Prepares the Network for a run.

Objects in the Network are sorted into the correct running order, and their BrianObject.before_run() methods are called.

Parameters: run_namespace : dict-like, optional A namespace in which objects which do not define their own namespace will be run.
check_dependencies()[source]
get_profiling_info(*args, **kwds)

The only reason this is not directly implemented in profiling_info is to allow devices (e.g. CPPStandaloneDevice) to overwrite this.

get_states(units=True, format='dict', subexpressions=False, read_only_variables=True, level=0)[source]

Return a copy of the current state variable values of objects in the network.. The returned arrays are copies of the actual arrays that store the state variable values, therefore changing the values in the returned dictionary will not affect the state variables.

Parameters: vars : list of str, optional The names of the variables to extract. If not specified, extract all state variables (except for internal variables, i.e. names that start with '_'). If the subexpressions argument is True, the current values of all subexpressions are returned as well. units : bool, optional Whether to include the physical units in the return value. Defaults to True. format : str, optional The output format. Defaults to 'dict'. subexpressions: bool, optional : Whether to return subexpressions when no list of variable names is given. Defaults to False. This argument is ignored if an explicit list of variable names is given in vars. read_only_variables : bool, optional Whether to return read-only variables (e.g. the number of neurons, the time, etc.). Setting it to False will assure that the returned state can later be used with set_states. Defaults to True. level : int, optional How much higher to go up the stack to resolve external variables. Only relevant if extracting subexpressions that refer to external variables. values : dict A dictionary mapping object names to the state variables of that object, in the specified format.
remove(*objs)[source]

Remove an object or sequence of objects from a Network.

Parameters: objs : (BrianObject, container) The BrianObject or container of Brian objects to be removed. Specify multiple objects, or lists (or other containers) of objects. Containers will be removed recursively.
restore(name='default', filename=None)

Retore the state of the network and all included objects.

Parameters: name : str, optional The name of the snapshot to restore, if not specified uses 'default'. filename : str, optional The name of the file from where the state should be restored. If not specified, it is expected that the state exist in memory (i.e. Network.store() was previously called without the filename argument).
run(duration, report=None, report_period=60*second, namespace=None, level=0)

Runs the simulation for the given duration.

Parameters: duration : Quantity The amount of simulation time to run for. report : {None, ‘text’, ‘stdout’, ‘stderr’, function}, optional How to report the progress of the simulation. If None, do not report progress. If 'text' or 'stdout' is specified, print the progress to stdout. If 'stderr' is specified, print the progress to stderr. Alternatively, you can specify a callback callable(elapsed, complete, duration) which will be passed the amount of time elapsed as a Quantity, the fraction complete from 0.0 to 1.0 and the total duration of the simulation (in biological time). The function will always be called at the beginning and the end (i.e. for fractions 0.0 and 1.0), regardless of the report_period. report_period : Quantity How frequently (in real time) to report progress. namespace : dict-like, optional A namespace that will be used in addition to the group-specific namespaces (if defined). If not specified, the locals and globals around the run function will be used. profile : bool, optional Whether to record profiling information (see Network.profiling_info). Defaults to True. level : int, optional How deep to go up the stack frame to look for the locals/global (see namespace argument). Only used by run functions that call this run function, e.g. MagicNetwork.run() to adjust for the additional nesting.

Notes

The simulation can be stopped by calling Network.stop() or the global stop() function.

scheduling_summary()[source]

Return a SchedulingSummary object, representing the scheduling information for all objects included in the network.

Returns: summary : SchedulingSummary Object representing the scheduling information.
set_states(values, units=True, format='dict', level=0)[source]

Set the state variables of objects in the network.

Parameters: values : dict A dictionary mapping object names to objects of format, setting the states of this object. units : bool, optional Whether the values include physical units. Defaults to True. format : str, optional The format of values. Defaults to 'dict' level : int, optional How much higher to go up the stack to _resolve external variables. Only relevant when using string expressions to set values.

Group.set_states()

stop()

Stops the network from running, this is reset the next time Network.run() is called.

store(name='default', filename=None)

Store the state of the network and all included objects.

Parameters: name : str, optional A name for the snapshot, if not specified uses 'default'. filename : str, optional A filename where the state should be stored. If not specified, the state will be stored in memory.

Notes

The state stored to disk can be restored with the Network.restore() function. Note that it will only restore the internal state of all the objects (including undelivered spikes) – the objects have to exist already and they need to have the same name as when they were stored. Equations, thresholds, etc. are not stored – this is therefore not a general mechanism for object serialization. Also, the format of the file is not guaranteed to work across platforms or versions. If you are interested in storing the state of a network for documentation or analysis purposes use Network.get_states() instead.