Recipes

An Arbor recipe is a description of a model. The recipe is queried during the model building phase to provide information about individual cells in the model, such as:

  • The number of cells in the model.

  • The kind of each cell.

  • The description of each cell, e.g. with morphology, dynamics, synapses, detectors, stimuli etc.

  • The number of spike targets on each cell.

  • The number of spike sources on each cell.

  • The number of gap junction sites on each cell.

  • Incoming network connections from other cells terminating on a cell.

  • Gap junction connections on each cell.

  • Probes on each cell.

Recipes are structured to provide a consistent interface for describing each cell in the network using their global identifier (gid). This allows the simulator to be able to quickly look-up properties related to the connections going in and out of a cell (think of synapses, gap junctions, but also probes and spike inputs); which helps make Arbor fast and easily distributable over many nodes.

To better illustrate the content of a recipe, let’s consider the following network of three cells:

  • Cell 0: Is a single soma, with hh (Hodgkin-huxley) dynamics. In the middle of the soma, a spike detector is attached, it generates a spiking event when the voltage goes above 10 mV. In the same spot on the soma, a current clamp is also attached, with the intention of triggering some spikes. All of the preceding info: the morphology, dynamics, spike detector and current clamp are what is referred to in Arbor as the description of the cell.
    Cell 0 should be modelled as a cable cell, (because cable cells allow complex dynamics such as hh). This is referred to as the kind of the cell.
    It’s quite expensive to build cable cells, so we don’t want to do this too often. But when the simulation is first set up, it needs to know how cells interact with one another in order to distribute the simulation over the available computational resources. This is why the number of targets, sources and gap junction sites is needed separately from the cell description: with them, the simulation can tell that cell 0 has 1 spike source (the detector), 0 spike targets, and 0 gap junction sites, without having to build the cell.
  • Cell 1: Is a soma and a single dendrite, with passive dynamics everywhere. It has a single synapse at the end of the dendrite and a gap junction site in the middle of the soma. This is the description of the cell. It’s also a cable cell, which is its cell kind. It has 0 spike sources, 1 spike target (the synapse) and 1 gap junction site.
  • Cell 2: Is a soma and a single dendrite, with passive dynamics everywhere. It has a gap junction site in the middle of the soma. This is the description of the cell. It’s also a cable cell, which is its cell kind. It has 0 spike sources, 0 spike targets and 1 gap junction site.

The total number of cells in the model is 3. The kind, description and number of spike sources, spike targets and gap junction sites on each cell is known and can be registered in the recipe. Next is the cell interaction.

The model is designed such that cell 0 has a spike source, cell 1 has a spike target and gap junction site, and cell 2 has a gap junction site. A network connection can be formed from cell 0 to cell 1; and a gap junction connection from cell 1 to cell 2. If cell 0 spikes, a spike should be observed on cell 2 after some delay. To monitor the voltage on cell 2 and record the spike, a probe can be set up on cell 2. All this information is also registered via the recipe.

There are additional docs on cell kinds; cell descriptions; network connections; gap junction connections; probes

The recipe is used to distribute the model across machines and is used in the simulation. Technical details of the recipe class are presented in the Python and C++ APIs.

Are recipes always necessary?

Yes. However, we provide a python single_cell_model that abstracts away the details of a recipe for simulations of single, stand-alone cable cells, which absolves the users from having to create the recipe themselves. This is possible because the number of cells, spike targets, spike sources and gap junction sites is fixed and known, as well as the fact that there can be no connections or gap junctions on a single cell. The single cell model is able to fill out the details of the recipe under the hood, and the user need only provide the cell description, and any probes they wish to place on the cell.

Why recipes?

The interface and design of Arbor recipes was motivated by the following aims:

  • Building a simulation from a recipe description must be possible in a distributed system efficiently with minimal communication.

  • Minimising the amount of memory used in model building, making it possible to build and run simulations in one run.

Recipe descriptions are cell-oriented, in order that the building phase can be efficiently distributed and that the model can be built independently of any runtime execution environment.

During model building, the recipe is queried first by a load balancer, then later when building the low-level cell groups and communication network. The cell-centered recipe interface, whereby cell and network properties are specified “per-cell”, facilitates this.

The steps of building a simulation from a recipe are:

1. Load balancing

First, the cells are partitioned over MPI ranks, and each rank parses the cells assigned to it to build a cost model. The ranks then coordinate to redistribute cells over MPI ranks so that each rank has a balanced workload. Finally, each rank groups its local cells into cell_group s that balance the work over threads (and GPU accelerators if available).

2. Model building

The model building phase takes the cells assigned to the local rank, and builds the local cell groups and the part of the communication network by querying the recipe for more information about the cells assigned to it.

Note

An example of how performance considerations impact Arbor’s architecture: you will notice cell kind and cell description are separately added to a recipe.

It might seem like overkill to have a separate call that returns the cell kind, when one could determine the kind by requesting the cell description, then querying the kind of the result.

Some phases of model construction, however, only require the cell kind, and not the full cell description, which can be quite expensive to assemble; for example, a Purkinje cell model can have very complex geometry, a rich collection of ion channels, and thousands of synapses.

General best practices

Think of the cells

When formulating a model, think cell-first, and try to formulate the model and the associated workflow from a cell-centred perspective. If this isn’t possible, please contact the developers, because we would like to develop tools that help make this simpler.

Be lazy

A recipe does not have to contain a complete description of the model in memory. Precompute as little as possible, and use lazy evaluation to generate information only when requested. This has multiple benefits, including:

  • thread safety;

  • minimising the memory footprint of the recipe.

Be reproducible

Arbor is designed to give reproducible results when the same model is run on a different number of MPI ranks or threads, or on different hardware (e.g. GPUs). This only holds when a recipe provides a reproducible model description, which can be a challenge when a description uses random numbers, e.g. to pick incoming connections to a cell from a random subset of a cell population. To get a reproducible model, use the cell gid (or a hash based on the gid) to seed random number generators, including those for event_generator s.