Brian2 allows to model the absolute refractory period of a neuron in a flexible way. The definition of refractoriness consists of two components: For what time after a spike is a neuron considered to be refractory and what changes in the neuron during the refractoriness.

Defining the refractory period

The refractory period is specified by the refractory keyword in the NeuronGroup initializer. In the simplest case, this is simply a fixed time, valid for all neurons:

G = NeuronGroup(N, model='...', threshold='...', reset='...',

Alternatively, it can be a string expression that evaluates to a time. This expression will be evaluated after every spike and allows for a changing refractory period. For example, the following will set the refractory period to a random duration between 1ms and 3ms after every spike:

G = NeuronGroup(N, model='...', threshold='...', reset='...',
                refractory='(1 + 2*rand())*ms')

In general, modelling a refractory period that varies across neurons involves declaring a state variable that stores the refractory period per neuron as a model parameter. The refractory expression can then refer to this parameter:

G = NeuronGroup(N, model='''...
                            refractory : second''', threshold='...',
                reset='...', refractory='refractory')
# Set the refractory period for each cell
G.refractory = ...

This state variable can also be a dynamic variable itself. For example, it can serve as an adaptation mechanism by increasing it after every spike and letting it relax back to a steady-state value between spikes:

refractory_0 = 2*ms
tau_refractory = 50*ms
G = NeuronGroup(N, model='''...
                            drefractory/dt = (refractory_0 - refractory) / tau_refractory : second''',
                threshold='...', refractory='refractory',
                         refractory += 1*ms''')
G.refractory = refractory_0

In some cases, the condition for leaving the refractory period is not easily expressed as a certain time span. For example, in a Hodgkin-Huxley type model the threshold is only used for counting spikes and the refractoriness is used to prevent to count multiple spikes for a single threshold crossing (the threshold condition would evaluate to True for several time points). Leaving the refractory period is not easily expressed as a time span but more naturally as a condition that the neuron should remain refractory for as long as it stays above the threshold. This can be achieved by using a string expression for the refractory keyword that evaluates to a boolean condition:

G = NeuronGroup(N, model='...', threshold='v > -20*mV',
                refractory='v >= -20*mV')

The refractory keyword should be read as “stay refractory as long as the condition remains true”. In fact, specifying a time span for the refractoriness will be automatically transformed into a logical expression using the current time t and the time of the last spike lastspike. Specifying refractory=2*ms is equivalent to specifying refractory='(t - lastspike) <= 2*ms'.

Defining model behaviour during refractoriness

The refractoriness definition as described above does only have a single effect by itself: threshold crossings during the refractory period are ignored (this is simply implemented by adding and not_refractory to the threshold condition).

In the following model, the variable v continues to update during the refractory period but it does not elicit a spike if it crosses the threshold:

G = NeuronGroup(N, 'dv/dt = -v / tau : 1',
                threshold='v > 1', reset='v=0',

There is also a second implementation of refractoriness that is supported by Brian, one or several state variables can be clamped during the refractory period. To model this kind of behaviour, variables that should stop being updated during refractoriness can be marked with the (unless refractory) flag:

G = NeuronGroup(N, '''dv/dt = -(v + w)/ tau_v : 1 (unless refractory)
                      dw/dt = -w / tau_w : 1''',
                threshold='v > 1', reset='v=0; w+=0.1', refractory=2*ms)

In the above model, the v variable is clamped at 0 for 2ms after a spike but the adaptation variable w continues to update during this time. Internally, this is implemented by adding * int(not_refractory) to the right-hand side of the respective differential equation, i.e. during refractoriness it is multiplied with zero. The same technique can also be used to model more complex behaviour during refractoriness. For example, the following code updates the w variable with a different time constant during refractoriness:

G = NeuronGroup(N, '''dv/dt = -(v + w)/ tau_v : 1 (unless refractory)
                      dw/dt = (-w / tau_active)*int(not_refractory) + (-w / tau_ref)*(1 - int(not_refractory)) : 1''',
                threshold='v > 1', reset='v=0; w+=0.1', refractory=2*ms)