# Running a simulation¶

To run a simulation, one either constructs a new Network object and calls its Network.run() method, or uses the “magic” system and a plain run() call, collecting all the objects in the current namespace.

Note that Brian has several different ways of running the actual computations, and choosing the right one can make orders of magnitude of difference in terms of simplicity and efficiency. See Computational methods and efficiency for more details.

## Networks¶

In most straightforward simulations, you do not have to explicitly create a Network object but instead can simply call run() to run a simulation. This is what is called the “magic” system, because Brian figures out automatically what you want to do.

When calling run(), Brian runs the collect() function to gather all the objects in the current context. It will include all the objects that are “visible”, i.e. that you could refer to with an explicit name:

G = NeuronGroup(10, 'dv/dt = -v / tau : volt')
S = Synapses(G, G, model='w:1', on_pre='v+=w')
S.connect('i!=j')
mon = SpikeMonitor(G)

run(10*ms)  # will include G, S, mon


Note that it will not automatically include objects that are “hidden” in containers, e.g. if you store several monitors in a list. Use an explicit Network object in this case. It might be convenient to use the collect() function when creating the Network object in that case:

G = NeuronGroup(10, 'dv/dt = -v / tau : volt')
S = Synapses(G, G, model='w:1', on_pre='v+=w')
S.connect('i!=j')
monitors = [SpikeMonitor(G), StateMonitor(G, 'v', record=True)]

# a simple run would not include the monitors
net = Network(collect())  # automatically include G and S

net.run(10*ms)


## Setting the simulation time step¶

To set the simulation time step for every simulated object, set the dt attribute of the defaultclock which is used by all objects that do not explicitly specify a clock or dt value during construction:

defaultclock.dt = 0.05*ms


If some objects should use a different clock (e.g. to record values with a StateMonitor not at every time step in a long running simulation), you can provide a dt argument to the respective object:

s_mon = StateMonitor(group, 'v', record=True, dt=1*ms)


To sum up:

• Set defaultclock.dt to the time step that should be used by most (or all) of your objects.
• Set dt explicitly when creating objects that should use a different time step.

Behind the scenes, a new Clock object will be created for each object that defines its own dt value.

## Progress reporting¶

Especially for long simulations it is useful to get some feedback about the progress of the simulation. Brian offers a few built-in options and an extensible system to report the progress of the simulation. In the Network.run() or run() call, two arguments determine the output: report and report_period. When report is set to 'text' or 'stdout', the progress will be printed to the standard output, when it is set to 'stderr', it will be printed to “standard error”. There will be output at the start and the end of the run, and during the run in report_period intervals. It is also possible to do custom progress reporting.

## Continuing/repeating simulations¶

To store the current state of the simulation, call store() (use the Network.store() method for a Network). You can store more than one snapshot of a system by providing a name for the snapshot; if store() is called without a specified name, 'default' is used as the name. To restore the state, use restore().

The following simple example shows how this system can be used to run several trials of an experiment:

# set up the network
G = NeuronGroup(...)
...
spike_monitor = SpikeMonitor(G)

# Snapshot the state
store()

# Run the trials
spike_counts = []
for trial in range(3):
restore()  # Restore the initial state
run(...)
# store the results
spike_counts.append(spike_monitor.count)


The following schematic shows how multiple snapshots can be used to run a network with a separate “train” and “test” phase. After training, the test is run several times based on the trained network. The whole process of training and testing is repeated several times as well:

# set up the network
G = NeuronGroup(..., '''...
test_input : amp
...''')
S = Synapses(..., '''...
plastic : boolean (shared)
...''')
G.v = ...
S.connect(...)
S.w = ...

# First snapshot at t=0
store('initialized')

# Run 3 complete trials
for trial in range(3):
# Simulate training phase
restore('initialized')
S.plastic = True
run(...)

# Snapshot after learning
store('after_learning')

# Run 5 tests after the training
for test_number in range(5):
restore('after_learning')
S.plastic = False  # switch plasticity off
G.test_input = test_inputs[test_number]
# monitor the activity now
spike_mon = SpikeMonitor(G)
run(...)
# Do something with the result
# ...


The following topics are not essential for beginners.

## Multiple magic runs¶

When you use more than a single run() statement, the magic system tries to detect which of the following two situations applies:

1. You want to continue a previous simulation
2. You want to start a new simulation

For this, it uses the following heuristic: if a simulation consists only of objects that have not been run, it will start a new simulation starting at time 0 (corresponding to the creation of a new Network object). If a simulation only consists of objects that have been simulated in the previous run() call, it will continue that simulation at the previous time.

If neither of these two situations apply, i.e., the network consists of a mix of previously run objects and new objects, an error will be raised. If this is not a mistake but intended (e.g. when a new input source and synapses should be added to a network at a later stage), use an explicit Network object.

In these checks, “non-invalidating” objects (i.e. objects that have BrianObject.invalidates_magic_network set to False) are ignored, e.g. creating new monitors is always possible.

## Changing the simulation time step¶

You can change the simulation time step after objects have been created or even after a simulation has been run:

defaultclock.dt = 0.1*ms
# Set the network
# ...
run(initial_time)
defaultclock.dt = 0.01*ms
run(full_time - initial_time)


To change the time step between runs for objects that do not use the defaultclock, you cannot directly change their dt attribute (which is read-only) but instead you have to change the dt of the clock attribute. If you want to change the dt value of several objects at the same time (but not for all of them, i.e. when you cannot use defaultclock.dt) then you might consider creating a Clock object explicitly and then passing this clock to each object with the clock keyword argument (instead of dt). This way, you can later change the dt for several objects at once by assigning a new value to Clock.dt.

## Profiling¶

To get an idea which parts of a simulation take the most time, Brian offers a basic profiling mechanism. If a simulation is run with the profile=True keyword argument, it will collect information about the total simulation time for each CodeObject. This information can then be retrieved from Network.profiling_info, which contains a list of (name, time) tuples or a string summary can be obtained by calling profiling_summary(). The following example shows profiling output after running the CUBA example (where the neuronal state updates take up the most time):

>>> profiling_summary(show=5)  # show the 5 objects that took the longest
Profiling summary
=================
neurongroup_stateupdater    5.54 s    61.32 %
synapses_pre                1.39 s    15.39 %
synapses_1_pre              1.03 s    11.37 %
spikemonitor                0.59 s     6.55 %
neurongroup_thresholder     0.33 s     3.66 %


## Scheduling¶

Every simulated object in Brian has three attributes that can be specified at object creation time: dt, when, and order. The time step of the simulation is determined by dt, if it is specified, or otherwise by defaultclock.dt. Changing this will therefore change the dt of all objects that don’t specify one.

During a single time step, objects are updated in an order according first to their when argument’s position in the schedule. This schedule is determined by Network.schedule which is a list of strings, determining “execution slots” and their order. It defaults to: ['start', 'groups', 'thresholds', 'synapses', 'resets', 'end']. In addition to the names provided in the schedule, names such as before_thresholds or after_synapses can be used that are understood as slots in the respective positions. The default for the when attribute is a sensible value for most objects (resets will happen in the reset slot, etc.) but sometimes it make sense to change it, e.g. if one would like a StateMonitor, which by default records in the end slot, to record the membrane potential before a reset is applied (otherwise no threshold crossings will be observed in the membrane potential traces).

Finally, if during a time step two objects fall in the same execution slot, they will be updated in ascending order according to their order attribute, an integer number defaulting to 0. If two objects have the same when and order attribute then they will be updated in an arbitrary but reproducible order (based on the lexicographical order of their names).

Every new Network starts a simulation at time 0; Network.t is a read-only attribute, to go back to a previous moment in time (e.g. to do another trial of a simulation with a new noise instantiation) use the mechanism described below.

For more details, including finer control over the scheduling of operations and changing the value of dt between runs see Scheduling and custom progress reporting.

## Store/restore¶

Note that Network.run(), Network.store() and Network.restore() (or run(), store(), restore()) are the only way of affecting the time of the clocks. In contrast to Brian1, it is no longer necessary (nor possible) to directly set the time of the clocks or call a reinit function.

The state of a network can also be stored on disk with the optional filename argument of Network.store()/store(). This way, you can run the initial part of a simulation once, store it to disk, and then continue from this state later. Note that the store()/restore() mechanism does not re-create the network as such, you still need to construct all the NeuronGroup, Synapses, StateMonitor, ... objects, restoring will only restore all the state variable values (membrane potential, conductances, synaptic connections/weights/delays, ...). This restoration does however restore the internal state of the objects as well, e.g. spikes that have not been delivered yet because of synaptic delays will be delivered correctly.