Simulation control

Initialising the simulator

Before using any other functions or classes from PyNN, the user must call the setup() function:

>>> setup()

setup() takes various optional arguments: setting the simulation timestep (there is currently no support in the API for variable timestep methods although native simulator code can be used to select this option where the simulator supports it) and setting the minimum and maximum synaptic delays, e.g.:

>>> setup(timestep=0.1, min_delay=0.1, max_delay=10.0)

Calling setup() a second time resets the simulator entirely, destroying any network that may have been created in the meantime.


add links to documentation on simulator-specific options to setup()

Getting information about the simulation state

Several functions are available for obtaining information about the simulation state:

  • get_current_time() - the time within the simulation
  • get_time_step() - the integration time step
  • get_min_delay() - the minimum allowed synaptic delay
  • get_max_delay() - the maximum allowed synaptic delay
  • num_processes() - the number of MPI processes
  • rank() - the MPI rank of the current node

Running a simulation

The run() function advances the simulation for a given number of milliseconds, e.g.:

>>> run(1000.0)

You can also use run_for(), which is an alias for run(). The run_until() function advances the simulation until a given future time point, e.g.:

>>> run_until(1001.0)
>>> get_current_time()

Performing operations during a run

You may wish to perform some calculation, or show some information, during a run. One way to do this is to break the simulation into steps, and perform the operation at the end of each step, e.g.:

>>> for i in range(4):
...    run_until(100.0*i)
...    print("The time is %g" % (100*i,))
The time is 0
The time is 100
The time is 200
The time is 300

Alternatively, PyNN can take care of breaking the simulation into steps for you. run() and run_until() each accept an optional list of callbacks functions. Each callback should accept the current time as an argument, and return the next time it wishes to be called.

>>> def report_time(t):
...     print("The time is %g" % t)
...     return t + 100.0
>>> run_until(300.0, callbacks=[report_time])
The time is 0
The time is 100
The time is 200
The time is 300

For simple cases, this requires a bit more code, but it is potentially much more powerful, especially if you have complex or multiple callbacks.

Repeating a simulation

If you wish to reset network time to zero to run a new simulation with the same network (with different parameter values, perhaps), use the reset() function. Note that this does not change the network structure, nor the choice of which neurons to record (from previous record() calls).

Finishing up

Just as a simulation must be begun with a call to setup(), it should be ended with a call to end(). This is not always necessary, but it is safest to always use it.