Equations

Equation strings

Equations are used both in NeuronGroup and Synapses to:

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

Equations are defined by multiline strings.

An Equation is a set of single lines in a string:

  1. dx/dt = f : unit (differential equation)
  2. x = f : unit (subexpression)
  3. x : unit (parameter)

Each equation may be spread out over multiple lines to improve formatting. Comments using # may also be included. Subunits are not allowed, i.e., one must write volt, not mV. This is to make it clear that the values are internally always saved in the basic 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.

Note

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.

Some special variables are defined: t, dt (time) and xi (white noise). 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. 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 variables and functions

Equations defining neuronal or synaptic equations can contain references to external parameters 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)
net.run(10*ms)

# 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
net.run(10*ms)

See Namespaces for more details.

The following topics are not essential for beginners.


Flags

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

event-driven
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.
constant
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.
shared
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.
linked
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.

dt
Time step width
i
Index of a neuron (NeuronGroup) or the pre-synaptic neuron of a synapse (Synapses)
j
Index of a post-synaptic neuron of a synapse
lastspike
Last time that the neuron spiked (for refractoriness)
lastupdate
Time of the last update of synaptic variables in event-driven equations.
N
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.
not_refractory
Boolean variable that is normally true, and false if the neuron is currently in a refractory state
t
Current time
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