Equation strings

Equations are used both in NeuronGroup and Synapses to:

  • define state variables

  • define continuous-updates on these variables, through differential equations


Brian models are defined by systems of first order ordinary differential equations, but you might see the integrated form of synapses in some textbooks and papers. See Converting from integrated form to ODEs for details on how to convert between these representations.

Equations are defined by multiline strings, where each line takes of one of three forms:

  1. dx/dt = f : unit (flags) (differential equation)

  2. x = f : unit (flags) (subexpression)

  3. x : unit (flags) (parameter)

Each of these definitions can take optional flags in parentheses after the unit declaration (see Flags below).

The first form defines a differential equation that determines how a variable evolves over time. The second form defines a subexpression, which is useful to make complex equations more readable, and to have a name for expressions that can be recorded with a StateMonitor. Such subexpressions are computed “on demand” and are not stored. Their use is therefore mostly for convenience and does not affect simulation time or memory usage. The third form defines a parameter, which is a value that is unique to each neuron or synapse. Its value can either be constant (e.g. to have a heterogeneous population of neurons) or can be a value that gets updated by synaptic events, or by run_regularly operations.

Each definition may be spread out over multiple lines to improve readability, and can include comments after #. The unit definition defines the dimension of the variable. Note that these are always the dimensions of the variable defined in the line, even in the case of differential equations. Therefore, the unit for the membrane potential would be volt and not volt/second (the dimensions of its derivative). The unit always has to be a base unit, i.e., one must write volt, not mV. This is to make it clear that the values are internally always saved in the base units, so no confusion can arise when getting the values out of a NeuronGroup and discarding the units. Compound units are of course allowed as well (e.g. farad/meter**2). There are also three special “units” that can be used: 1 denotes a dimensionless floating point variable, boolean and integer denote dimensionless variables of the respective kind.


For molar concentration, the base unit that has to be used in the equations is mmolar (or mM), not molar. This is because 1 molar is 10³ mol/m³ in SI units (i.e., it has a “scale” of 10³), whereas 1 millimolar corresponds to 1 mol/m³.

Arithmetic operations and functions

Equation strings can make use of standard arithmetic operations for numerical values, using the Python 3 syntax. The supported operations are +, -, *, / (floating point division), // (flooring division), % (remainder), ** (power). For variable assignments, e.g. in reset statements, the corresponding in-place assignments such as += can be used as well. For comparisons, the operations == (equality), != (inequality), <, <=, >, and >= are available. Truth values can be combined using and and or, or negated using not. Note that Brian does not support any operations specific to integers, e.g. “bitwise AND” or shift operations. Importantly, while equations use Python syntax, they are not Python code; they are parsed and translated to the target language by Brian, and can therefore not use arbitrary Python syntax or functions. They are also written in a “for each neuron/synapse” style, so their interpretation depends on the context in which they are used. For example, when a synaptic pre/post statement refers to a variable of a pre- or post-synaptic neurons, it only refers to the subset of neurons that spiked. This also means that you cannot (and usually don’t need to) use Python’s indexing syntax to refer to specific elements of a group.


Brian versions up to did not support // as the floor division operator and potentially used different semantics for the / operator depending on whether Python 2 or 3 was used. To write code that correctly and unambiguously works with both newer and older Brian versions, you can use expressions such as 1.0*a/b to enforce floating point division (if one of the operands is a floating point number, both Python 2 and 3 will use floating point division), or floor(a/b) to enforce flooring division Note that the floor function always returns a floating point value, if it is important that the result is an integer value, additionally wrap it with the int function.

Brian also supports standard mathematical functions with the same names as used in the numpy library (e.g. exp, sqrt, abs, clip, sin, cos, …) – for a full list see Default functions. Note that support for such functions is provided by Brian itself and the translation to the various code generation targets is automatically taken care of. You should therefore refer to them directly by name and not as e.g. np.sqrt or numpy.sqrt, regardless of the way you imported Brian or numpy. This also means that you cannot directly refer to arbitrary functions from numpy or other libraries. For details on how to extend the support to non-default functions see User-provided functions.

Special variables

Some special variables are defined, e.g. t, dt (time) and xi (white noise). For a full list see List of special symbols below. Variable names starting with an underscore and a couple of other names that have special meanings under certain circumstances (e.g. names ending in _pre or _post) are forbidden.

For stochastic equations with several xi values it is necessary to make clear whether they correspond to the same or different noise instantiations. To make this distinction, an arbitrary suffix can be used, e.g. using xi_1 several times refers to the same variable, xi_2 (or xi_inh, xi_alpha, etc.) refers to another. An error will be raised if you use more than one plain xi without any suffix. Note that noise is always independent across neurons, you can only work around this restriction by defining your noise variable as a shared parameter and update it using a user-defined function (e.g. with run_regularly), or create a group that models the noise and link to its variable (see Linked variables).

External references

Equations defining neuronal or synaptic equations can contain references to external constants or functions. These references are looked up at the time that the simulation is run. If you don’t specify where to look them up, it will look in the Python local/global namespace (i.e. the block of code where you call run()). If you want to override this, you can specify an explicit “namespace”. This is a Python dictionary with keys being variable names as they appear in the equations, and values being the desired value of that variable. This namespace can be specified either in the creation of the group or when you can the run() function using the namespace keyword argument.

The following three examples show the different ways of providing external variable values, all having the same effect in this case:

# Explicit argument to the NeuronGroup
G = NeuronGroup(1, 'dv/dt = -v / tau : 1', namespace={'tau': 10*ms})
net = Network(G)

# Explicit argument to the run function
G = NeuronGroup(1, 'dv/dt = -v / tau : 1')
net = Network(G)
net.run(10*ms, namespace={'tau': 10*ms})

# Implicit namespace from the context
G = NeuronGroup(1, 'dv/dt = -v / tau : 1')
net = Network(G)
tau = 10*ms

See Namespaces for more details.

The following topics are not essential for beginners.


A flag is a keyword in parentheses at the end of the line, which qualifies the equations. There are several keywords:


this is only used in Synapses, and means that the differential equation should be updated only at the times of events. This implies that the equation is taken out of the continuous state update, and instead a event-based state update statement is generated and inserted into event codes (pre and post). This can only qualify differential equations of synapses. Currently, only one-dimensional linear equations can be handled (see below).

unless refractory

this means the variable is not updated during the refractory period. This can only qualify differential equations of neuron groups.


this means the parameter will not be changed during a run. This allows optimizations in state updaters. This can only qualify parameters.

constant over dt

this means that the subexpression will be only evaluated once at the beginning of the time step. This can be useful to e.g. approximate a non-linear term as constant over a time step in order to use the linear numerical integration algorithm. It is also mandatory for subexpressions that refer to stateful functions like rand() to make sure that they are only evaluated once (otherwise e.g. recording the value with a StateMonitor would re-evaluate it and therefore not record the same values that are used in other places). This can only qualify subexpressions.


this means that a parameter or subexpression is not neuron-/synapse-specific but rather a single value for the whole NeuronGroup or Synapses. A shared subexpression can only refer to other shared variables.


this means that a parameter refers to a parameter in another NeuronGroup. See Linked variables for more details.

Multiple flags may be specified as follows:

dx/dt = f : unit (flag1,flag2)

List of special symbols

The following lists all of the special symbols that Brian uses in equations and code blocks, and their meanings.


Time step width


Index of a neuron (NeuronGroup) or the pre-synaptic neuron of a synapse (Synapses)


Index of a post-synaptic neuron of a synapse


Last time that the neuron spiked (for refractoriness)


Time of the last update of synaptic variables in event-driven equations (only defined when event-driven equations are used).


Number of neurons (NeuronGroup) or synapses (Synapses). Use N_pre or N_post for the number of presynaptic or postsynaptic neurons in the context of Synapses.


Boolean variable that is normally true, and false if the neuron is currently in a refractory state


Current time


Current time measured in time steps

xi, xi_*

Stochastic differential in equations

Event-driven equations

Equations defined as event-driven are completely ignored in the state update. They are only defined as variables that can be externally accessed. There are additional constraints:

  • An event-driven variable cannot be used by any other equation that is not also event-driven.

  • An event-driven equation cannot depend on a differential equation that is not event-driven (directly, or indirectly through subexpressions). It can depend on a constant parameter.

Currently, automatic event-driven updates are only possible for one-dimensional linear equations, but this may be extended in the future.

Equation objects

The model definitions for NeuronGroup and Synapses can be simple strings or Equations objects. Such objects can be combined using the add operator:

eqs = Equations('dx/dt = (y-x)/tau : volt')
eqs += Equations('dy/dt = -y/tau: volt')

Equations allow for the specification of values in the strings, but does this by simple string replacement, e.g. you can do:

eqs = Equations('dx/dt = x/tau : volt', tau=10*ms)

but this is exactly equivalent to:

eqs = Equations('dx/dt = x/(10*ms) : volt')

The Equations object does some basic syntax checking and will raise an error if two equations defining the same variable are combined. It does not however do unit checking, checking for unknown identifiers or incorrect flags – all this will be done during the instantiation of a NeuronGroup or Synapses object.

Examples of Equation objects

Concatenating equations

>>> membrane_eqs = Equations('dv/dt = -(v + I)/ tau : volt')
>>> eqs1 = membrane_eqs + Equations('''I = sin(2*pi*freq*t) : volt
...                                    freq : Hz''')
>>> eqs2 = membrane_eqs + Equations('''I : volt''')
>>> print(eqs1)
I = sin(2*pi*freq*t) : V
dv/dt = -(v + I)/ tau : V
freq : Hz
>>> print(eqs2)
dv/dt = -(v + I)/ tau : V
I : V

Substituting variable names

>>> general_equation = 'dg/dt = -g / tau : siemens'
>>> eqs_exc = Equations(general_equation, g='g_e', tau='tau_e')
>>> eqs_inh = Equations(general_equation, g='g_i', tau='tau_i')
>>> print(eqs_exc)
dg_e/dt = -g_e / tau_e : S
>>> print(eqs_inh)
dg_i/dt = -g_i / tau_i : S

Inserting values

>>> eqs = Equations('dv/dt = mu/tau + sigma/tau**.5*xi : volt',
...                  mu=-65*mV, sigma=3*mV, tau=10*ms)
>>> print(eqs)
dv/dt = (-65. * mvolt)/(10. * msecond) + (3. * mvolt)/(10. * msecond)**.5*xi : V