# Namespaces¶

Equations can contain references to external parameters or functions. During the initialisation of a NeuronGroup or a Synapses object, this namespace can be provided as an argument. This is a group-specific namespace that will only be used for names in the context of the respective group. Note that units and a set of standard functions are always provided and should not be given explicitly. This namespace does not necessarily need to be exhaustive at the time of the creation of the NeuronGroup/Synapses, entries can be added (or modified) at a later stage via the namespace attribute (e.g. G.namespace['tau'] = 10*ms).

At the point of the call to the Network.run() namespace, any group-specific namespace will be augmented by the “run namespace”. This namespace can be either given explicitly as an argument to the run method or it will be taken from the locals and globals surrounding the call. A warning will be emitted if a name is defined in more than one namespace.

To summarize: an external identifier will be looked up in the context of an object such as NeuronGroup or Synapses. It will follow the following resolution hierarchy:

1. Default unit and function names.
2. Names defined in the explicit group-specific namespace.
3. Names in the run namespace which is either explicitly given or the implicit namespace surrounding the run call.

Note that if you completely specify your namespaces at the Group level, you should probably pass an empty dictionary as the namespace argument to the run call – this will completely switch off the “implicit namespace” mechanism.

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)


External variables are free to change between runs (but not during one run), the value at the time of the run() call is used in the simulation.