Running a simulation

To run a simulation, one either constructs a new Network object and calls its 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.


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 / (10*ms) : 1',
                threshold='v > 1', reset='v = 0')
S = Synapses(G, G, model='w:1', on_pre='v+=w')
S.w = 'rand()'
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 / (10*ms) : 1',
                threshold='v > 1', reset='v = 0')
S = Synapses(G, G, model='w:1', on_pre='v+=w')
S.w = 'rand()'
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.add(monitors)  # manually add the monitors*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 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 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

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

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.w = ...

# First snapshot at t=0

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

    # Snapshot after learning

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

Note that if you do not want to run an object for the complete duration of your simulation, you can create the object in the beginning of your simulation and then set its active attribute. For details, see the Scheduling section below.

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
# ...
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.

Note that a change of dt has to be compatible with the internal representation of clocks as an integer value (the number of elapsed time steps). For example, you can simulate an object for 100ms with a time step of 0.1ms (i.e. for 1000 steps) and then switch to a dt of 0.5ms, the time will then be internally represented as 200 steps. You cannot, however, switch to a dt of 0.3ms, because 100ms are not an integer multiple of 0.3ms.


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. For convenience, a string summary can be obtained by calling profiling_summary() (which will automatically refer to the current “magic” network). The following example shows profiling output after running the CUBA example (where the neuronal state updates take up the most time):

>>> from brian2 import profiling_summary
>>> 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 %

If you use an explicit Network object, you need to pass it to profiling_summary:

>>> net = Network(...) 
>>> profiling_summary(net, ...) 


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. Alternatively, a clock object can be specified directly, this can be useful if a clock should be shared between several objects – under most circumstances, however, a user should not have to deal with the creation of Clock objects and just define dt.

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 start 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).

Note that objects that don’t do any computation by themselves but only act as a container for other objects (e.g. a NeuronGroup which contains a StateUpdater, a Resetter and a Thresholder), don’t have any value for when, but pass on the given values for dt and order to their containing objects.

If you want your simulation object to run only for a particular time period of the whole simulation, you can use the active attribute. For example, this can be useful when you want a monitor to be active only for some time out of a long simulation:

# Set up the network
# ...
monitor = SpikeMonitor(...) = False
run(long_time*seconds)  # not recording = True
run(required_time*seconds)  # recording

To see how the objects in a network are scheduled, you can use the scheduling_summary() function:

>>> group = NeuronGroup(10, 'dv/dt = -v/(10*ms) : 1', threshold='v > 1',
...                     reset='v = 0')
>>> mon = StateMonitor(group, 'v', record=True, dt=1*ms)
>>> scheduling_summary()  
                object                  |           part of           |        Clock dt        |    when    | order | active
statemonitor (StateMonitor)             | statemonitor (StateMonitor) | 1. ms (every 10 steps) | start      |     0 |  yes
neurongroup_stateupdater (StateUpdater) | neurongroup (NeuronGroup)   | 100. us (every step)   | groups     |     0 |  yes
neurongroup_thresholder (Thresholder)   | neurongroup (NeuronGroup)   | 100. us (every step)   | thresholds |     0 |  yes
neurongroup_resetter (Resetter)         | neurongroup (NeuronGroup)   | 100. us (every step)   | resets     |     0 |  yes

As you can see in the output above, the StateMonitor will only record the membrane potential every 10 time steps, but when it does, it will do it at the start of the time step, before the numerical integration, the thresholding, and the reset operation takes place.

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.


Note that, 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 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.