All equations, expressions and statements in Brian can make use of mathematical functions. However, functions have to be prepared for use with Brian for two reasons: 1) Brian is strict about checking the consistency of units, therefore every function has to specify how it deals with units; 2) functions need to be implemented differently for different code generation targets.

Brian provides a number of default functions that are already prepared for use with numpy and C++ and also provides a mechanism for preparing new functions for use (see below).

Default functions

The following functions (stored in the DEFAULT_FUNCTIONS dictionary) are ready for use:

  • Random numbers: rand(), randn() (Note that these functions should be called without arguments, the code generation process will take care of generating an array of numbers for numpy).
  • Elementary functions: sqrt, exp, log, log10, abs
  • Trigonometric functions: sin, cos, tan, sinh, cosh, tanh, arcsin, arccos, arctan
  • General utility functions: clip, floor, ceil

Brian also provides a special purpose function int_, which can be used to convert a boolean expression or variable into an integer value of 0 or 1. This is useful to have a conditional evaluation as part of an equation or statement. This sometimes allows to circumvent the lack of an if statement. For example, the following reset statement resets the variable v to either v_r1 or v_r2, depending on the value of w: 'v = v_r1 * int_(w <= 0.5) + v_r2 * int_(w > 0.5)'

User-provided functions

Python code generation

If a function is only used in contexts that use Python code generation, preparing a function for use with Brian only means specifying its units. The simplest way to do this is to use the check_units() decorator:

@check_units(x1=meter, y1=meter, x2=meter, y2=meter, result=meter)
def distance(x1, y1, x2, y2):
    return np.sqrt((x1 - x2)**2 + (y1 - y2)**2)

Another option is to wrap the function in a Function object:

def distance(x1, y1, x2, y2):
    return np.sqrt((x1 - x2)**2 + (y1 - y2)**2)
# wrap the distance function
distance = Function(distance, arg_units=[meter, meter, meter, meter],

The use of Brian’s unit system has the benefit of checking the consistency of units for every operation but at the expense of performance. Consider the following function, for example:

@check_units(I=amp, result=Hz)
def piecewise_linear(I):
    return np.clip((I-1*nA) * 50*Hz/nA, 0*Hz, 100*Hz)

When Brian runs a simulation, the state variables are stored and passed around without units for performance reasons. If the above function is used, however, Brian adds units to its input argument so that the operations inside the function do not fail with dimension mismatches. Accordingly, units are removed from the return value so that the function output can be used with the rest of the code. For better performance, Brian can alter the namespace of the function when it is executed as part of the simulation and remove all the units, then pass values without units to the function. In the above example, this means making the symbol nA refer to 1e-9 and Hz to 1. To use this mechanism, add the decorator make_functions with the discard_units keyword:

@check_units(I=amp, result=Hz)
def piecewise_linear(I):
    return np.clip((I-1*nA) * 50*Hz/nA, 0*Hz, 100*Hz)

Note that the use of the function outside of simulation runs is not affected, i.e. using piecewise_linear still requires a current in Ampere and returns a rate in Hertz. The discard_units mechanism does not work in all cases, e.g. it does not work if the function refers to units as brian2.nA instead of nA, if it uses imports inside the function (e.g. from brian2 import nA), etc. The discard_units can also be switched on for all functions without having to use the make_function() decorator by setting the codegen.runtime.numpy.discard_units preference.

Other code generation targets

To make a function available for other code generation targets (e.g. C++), implementations for these targets have to be added. This can be achieved using the make_function() decorator. The form of the code (e.g. a simple string or a dictionary of strings) necessary is target-dependent. An implementation for the C++ target could look like this:

                     double piecewise_linear(I) {
                        if (I < 1e-9)
                            return 0;
                        if (I > 3e-9)
                            return 100;
                        return (I/1e-9 - 1) * 50;
@check_units(I=amp, result=Hz)
def piecewise_linear(I):
    return np.clip((I-1*nA) * 50*Hz/nA, 0*Hz, 100*Hz)

Alternatively, FunctionImplementation objects can be added to the Function object. For a more complex example that also makes the function contribute additional values to the namespace of a CodeObject see TimedArray.