Networks can be regarded as graphs, where the nodes are locations on cells and the edges describe the communications between them. In Arbor, two sorts of edges are modelled: a connection abstracts the propagation of action potentials (spikes) through the network, while a gap junction connection is used to describe a direct electrical connection between two locations on two cells. Connections only capture the propagation delay and attenuation associated with spike connectivity: the biophysical modelling of the chemical synapses themselves is the responsibility of the target cell model.

Connection sites and gap junction sites are defined on locations on cells as part of the cell description. These sites as such are not connected yet, however the recipe exposes a number of callbacks to form connections and gap junctions between sites. The recipe callbacks are interrogated during simulation creation.

In addition, simulations may update their connectivity by building a new connection table outside calls to run, for example

rec = recipe()
dec = arb.domain_decomposition(rec, ctx)
sim = arb.simulation(rec, ctx, dec)

# run simulation for 0.25ms with the basic connectivity, 0.025)

# extend the recipe to more connections
#  use `connections_on` to build a new connection table

# run simulation for 0.25ms with the extended connectivity, 0.025)

This will completely replace the old table, previous connections to be retained must be explicitly included in the updated callback. This can also be used to update connection weights and delays. Note, however, that there is currently no way to introduce new sites to the simulation, nor any changes to gap junctions.

The update_connections method accepts either a full recipe (but will only use the connections_on and events_generators callbacks) or a connectivity, which is a reduced recipe exposing only the relevant callbacks. Currently connectivity is only available in C++; Python users have to pass a full recipe.


Arbor uses a lazily constructed network (from the recipe callbacks) for good reason; storing the full connectivity (for all gids) in the recipe can lead to probitivively large memory footprints. Keep this in mind when designing your connectivity and heed the consequences of doing I/O in these callbacks. This is doubly important when using models with dynamic connectivity where the temptation to store all connections is even larger and each call to update will re-evaluate the corresponding callbacks.


Connections implement chemical synapses between source and target cells and are characterized by having a transmission delay.

Connections in Arbor are defined in two steps:

  1. Create labeled source and target on two separate cells as part of their cell descriptions in the recipe. Sources typically generate spikes. Targets are typically synapses with associated biophysical model descriptions. Each labeled group of sources or targets may contain multiple items on possibly multiple locations on the cell.

  2. Declare the connection in the recipe on the target cell: from a source identified using a global_label; a target identified using a local_label (gid of target is the argument of the recipe method); a connection delay and a connection weight.

    def connections_on(self, gid):
        if gid + 1 < self.num_cells():
            return [arbor.connection((gid + 1, "spike-source"), "synapse", weight, delay)]
            return []
action potential

Spikes travel over connections. In a synapse, they generate an event.

threshold detector

Placed on a cell. Possible source of a connection. Detects crossing of a fixed threshold and generates corresponding events. Also used to record spikes for analysis. See here for more information.

spike source cell

Artificial cell to generate spikes on a given schedule, see spike cell.


By default, spikes are used for communication, but not stored for analysis, however, simulation objects can be instructed to record spikes.


In a synapse spikes generate events, which constitute stimulation of the synapse mechanism and the transmission of a signal. A synapse may receive events directly from an event generator.

event generator

Externally stimulate a synapse. Events can be delivered on a schedule. See arbor.event_generator for details.

gap junction connection

Gap junctions represent electrical synapses where transmission between cells is bidirectional and direct. They are modelled as a conductance between two gap junction sites on two cells.

Similarly to Connections, Gap Junctions in Arbor are defined in two steps:

  1. Create labeled gap junction sites on two separate cells as part of their cell descriptions in the recipe. Each labeled group of gap junctions may contain multiple items on possibly multiple locations on the cell.

  2. Declare the Gap Junction connections in the recipe on the local cell: from a peer gap junction site identified using a global_label; to a local gap junction site identified using a local_label (gid of the site is implicitly known); and a unit-less connection weight. Two of these connections are needed, on each of the peer and local cells. The callback gap_junctions_on returns a list of these items, eg

    def gap_junctions_on(self, gid):
        n = self.num_cells
        if gid + 1 < n and gid > 0:
            return [arbor.gap_junction_connection((gid + 1, "gj"), "gj", weight),
                    arbor.gap_junction_connection((gid - 1, "gj"), "gj", weight),]
        elif gid + 1 < n:
            return [arbor.gap_junction_connection((gid + 1, "gj"), "gj", weight),]
        if gid > 0:
            return [arbor.gap_junction_connection((gid - 1, "gj"), "gj", weight),]
            return []

    Note that gap junction connections are symmetrical and thus the above example generates two connections, one incoming and one outgoing.