Functions¶

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, sign
• 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 an expression or variable into an integer value. This is especially useful for boolean values (which will be converted into 0 or 1), for example to have a conditional evaluation as part of an equation or statement which 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 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 sqrt((x1 - x2)**2 + (y1 - y2)**2)
# wrap the distance function
distance = Function(distance, arg_units=[meter, meter, meter, meter],
return_unit=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 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 implementation() with the discard_units keyword:

@implementation('numpy', discard_units=True)
@check_units(I=amp, result=Hz)
def piecewise_linear(I):
return 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 implementation() 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 implementation() decorator. The form of the code (e.g. a simple string or a dictionary of strings) necessary is target-dependent, for C++ both options are allowed, a simple string will be interpreted as filling the 'support_code' block. Note that both 'cpp' and 'weave' can be used to provide C++ implementations, the first should be used for generic C++ implementations, and the latter if weave-specific code is necessary. An implementation for the C++ target could look like this:

@implementation('cpp', '''
double piecewise_linear(double 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 clip((I-1*nA) * 50*Hz/nA, 0*Hz, 100*Hz)


Alternatively, FunctionImplementation objects can be added to the Function object.

The same sort of approach as for C++ works for Cython using the 'cython' target. The example above would look like this:

@implementation('cython', '''
cdef double piecewise_linear(double I):
if I<1e-9:
return 0.0
elif I>3e-9:
return 100.0
return (I/1e-9-1)*50
''')
@check_units(I=amp, result=Hz)
def piecewise_linear(I):
return clip((I-1*nA) * 50*Hz/nA, 0*Hz, 100*Hz)


Arrays vs. scalar values in user-provided functions¶

Equations, expressions and abstract code statements are always implicitly referring to all the neurons in a NeuronGroup, all the synapses in a Synapses object, etc. Therefore, function calls also apply to more than a single value. The way in which this is handled differs between code generation targets that support vectorized expressions (e.g. the numpy target) and targets that don’t (e.g. the weave target or the cpp_standalone mode). If the code generation target supports vectorized expressions, it will receive an array of values. For example, in the piecewise_linear example above, the argument I will be an array of values and the function returns an array of values. For code generation without support for vectorized expressions, all code will be executed in a loop (over neurons, over synapses, ...), the function will therefore be called several times with a single value each time.

In both cases, the function will only receive the “relevant” values, meaning that if for example a function is evaluated as part of a reset statement, it will only receive values for the neurons that just spiked.

Some functions need additional data to compute a result, e.g. a TimedArray needs access to the underlying array. For the numpy target, a function can simply use a reference to an object defined outside the function, there is no need to explicitly pass values in a namespace. For the other code language targets, values can be passed in the namespace argument of the implementation() decorator or the add_implementation method. The namespace values are then accessible in the function code under the given name, prefixed with _namespace. Note that this mechanism should only be used for numpy arrays or general objects (e.g. function references to call Python functions from weave or Cython code). Scalar values should be directly included in the function code, by using a “dynamic implemention” (see add_dynamic_implementation).

See TimedArray and BinomialFunction for examples that use this mechanism.

Data types¶

By default, functions are assumed to take any type of argument, and return a floating point value. If you want to put a restriction on the type of an argument, or specify that the return type should be something other than float, either declare it as a Function (and see its documentation on specifying types) or use the declare_types() decorator, e.g.:

@check_units(a=1, b=1, result=1)
@declare_types(a='integer', result='highest')
def f(a, b):
return a*b


This is potentially important if you have functions that return integer or boolean values, because Brian’s code generation optimisation step will make some potentially incorrect simplifications if it assumes that the return type is floating point.