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.


The semantics of connection updates are subtle and might produce surprising results if handled carelessly. In particular, spikes in-flight over a connection will always be delivered, even if the connection has been deleted before the time of delivery has passed (= t_emitted + connection_delay). As Arbor’s connection model joins processes on the axon, the synaptic cleft, and the receiving synapse into a simple pair (weight, delay) it is unclear ‘where’ the action potential is located at the time of deletion relative to the locus of disconnection. Thus, it was decided to deliver spike events regardless. This is will not cause issues when the transition is slow and smooth, ie weights decays over time towards a small value and then the connection is removed. However, drastic and/or frequent changes across busy synapses might cause unexpected behaviour.


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 prohibitively 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.

Cross-Simulator Interaction#

This section describes how external simulators communicating via spikes can be connected to Arbor. For other methods of communication, translation to spikes, e.g. from neural mass models, is needed. For coupling to microscopic simulations, e.g. of individual ion channels, a different API is required. The mechanism ABI might be a good fit there.

The usual recipe can be used to declare connections to the world outside of Arbor similar to how internal (=both source and target are Arbor’s responsibility) connections are handled.

struct recipe(arb::recipe) {
  // Rest as ever before
  std::vector<arb::ext_cell_connection> external_connections_on(arb::cell_gid_type) const override {
      return {{arb::cell_remote_label_type{42,  // External GID
                                           23}, // per-gid tag


class recipe(arb.recipe):
    # Rest as ever before
    def external_connections_on(self, gid):
        return [arb.connection((42,      # external GID
                                32),     # tag

Note that Arbor now recognizes two sets of GID: An external and an internal set. This allows both Arbor and the coupled simulation to keep their own numbering schemes. However, internally Arbor will tag external cells and spikes by setting their GIDs’ most significant bit. This _halves_ the effecively available GIDs.

To consume external spike events, a specialised context must be created by calling

auto ctx = arb::make_context({}, local, inter);

or similarly in Python

ctx = arb.make_context(mpi=local, inter=inter)

where local is an MPI intracommunicator and inter an MPI intercommunicator. inter is required to bridge the Arbor (local) and external simulator’s respective MPI communicators. Note, that the exchange protocol _requires_ the semantics of an intercommunicator, passing anything else will result in an exception. You can create an intercommunicator in two main ways. First by splitting a pre-existing intercommunicator using MPI_Comm_split(4) and then calling MPI_Intercomm_create(7) on the result. This approach produces a single binary that goes down two different route, one calling Arbor and the other coupled simulation. Our remote example works this way. Second, using MPI_Comm_connect(5) and MPI_Comm_accept(5) will result in two completely separate binaries that can communicate over the generated intercommunicator. Please consult the MPI documentation for more details on these methods.

Data Plane and Spike Exchange#

The actual communication is performed in two steps, one to collect the number spikes from each participating task via MPI_Allgather(7) and the second to transfer the actual payload by MPI_Allgatherv(8). Note that over an intercommunicator, allgather will work slightly unintuitively by concatenating all results of a given ‘side’ of the intercommunicator and broadcasting that to the other ‘side’ and vice-versa. For example, assume Arbor has three MPI tasks, sending a0, a1, and a2 respectively and the coupled package has two tasks, sending b0 and b1. After allgather, each of the three Arbor ranks will have [b0, b1] and the two ranks of the other side will have [a0, a1, a2] each. We package this in the suplemental header arbor/communication/remote.hpp as gather_spikes. This function will accept a std::vector<arb_spike> where arb_spike is a binary compatible version of Arbor’s internal spike type that is to be sent from the local rank of the coupled packaged, eg b1 from above. After the operation Arbor has received the concatenation of all such vectors and the routine will return the concatenation of all spikes produced and exported by Arbor on all ranks of the participating package.

Please refer to our developer’s documentation for more details the actual spike exchange process. Due to the way MPI defines intercommunicators, the exchange is the same as with intracommunicators.

Control Plane and Epochs#

Before initiating the actual simulation, Arbor sets the epoch length to half the minimal delay in the global network. The minimal delay can be queried using simulation::min_delay and the epoch length is given by simulation::max_epoch_length. The final epoch is optionally shorter, if the call to simulation::run(T, dt) is given a value for T that is not an integer multiple of the epoch length.

Before the start of each epoch, a control message must be exchanged between Arbor and the coupled simulation. The control message is transferred by use MPI_Allreduce(6) with operation MPI_SUMM on a byte buffer of length ARB_REMOTE_MESSAGE_LENGTH. All processes begin with a buffer of zeroes, the process with rank equal to ARB_REMOTE_ROOT on both sides of the intercommunicator writes a payload comprising

  1. A single byte magic number

  2. A three byte version number

  3. A single byte message tag

  4. A binary representation of a C struct message

to its buffer. Then, the exhange is performed. This peculiar protocol yields a simultaneous exchange in both directions across the intercommunicator without taking order into consideration.

All constants and types – including the messages – are defined in arbor/communication/remote.hpp; currently Arbor understands and utilises the following message types:

If abort is received or sent Arbor will shut down at the next possible moment without performing any further work and potentially terminating all outstanding communication. An exception will be raised. Note that Arbor might terminate even without sending or receiving an abort message in exceptional circumstances.

On epoch Arbor will commence the next epoch. Note that Arbor may expect the last epoch to be shortened, ie when the total runtime is not a multiple of the epoch length.

Done signals the sending side is finished with the current simulation period, i.e. the current call to, dt). May cause the receiving side to quit.

Null does nothing, but reserved for future use, will currently not be sent by Arbor.

We package these messsage as a C++ std::variant called ctrl_message in arbor/communication/remote.hpp alongside the exchange_ctrl method. This will handle setting up the buffers, performing the actual transfer, and returns the result as a ctrl_messge. Handling the message is left to the participating package.

Important This is a synchronous protocol which means an unannounced termination of either side of the coupled simulators can lead to the other getting stuck on a blocking call to MPI. This unlikely to cause issues in scenarios where both sides are launched as a single job (eg via SLURM), but might do so where unrelated jobs are used.

Tying It All Together#

While there is no requirement on doing, we strongly recommend to make use of the facilities offered in arbor/communication/remote.hpp, as does Arbor internally. It should also be possible to interact with this protocol via C or other languages, if needed, as the infrastructure relies on byte-buffers and numeric tags; the use of C++ types and variants on top is just an attempt to make the interaction a bit safer and nicer. Refer to the remote.cpp example on how they are used and the inline comments in remote.hpp.

Terms and Definitions#


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.