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.
Incoming network connections from other cells terminating on a cell.
Gap junction connections on each cell.
Probes on each cell.
An example model¶
(location 0 0.5)
(location 0 0.5)
(location 0 0.5)
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. A threshold detector labelled “detector_0” is attached to the middle of the soma. The detector will generate a spike event when the voltage goes above 10 mV. At 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, threshold detector and current clamp — constitute what is termed the description of the cell.
Cell 0should 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.
Cell 1: Is a soma and a single dendrite, with
passivedynamics everywhere. It has a single synapse at the end of the dendrite labeled “synapse_1” and a gap junction mechanism in the middle of the soma labeled “gap_junction_1”. This is the description of the cell. It’s also a cable cell, which is its cell kind.
Cell 2: Is a soma and a single dendrite, with
passivedynamics everywhere. It has a gap junction mechanism in the middle of the soma labeled “gap_junction_2”. This is the description of the cell. It’s also a cable cell, which is its cell kind.
The total number of cells in the model is 3. The kind, and description of each cell is known and can be registered in the recipe. Next is the cell interaction.
The model is designed such that each cell has labeled source, target and gap junction sites.
A network connection can be formed from
synapse_1; and a
gap junction connection between
detector_0 spikes, a spike should be observed on
gap_junction_2 after some delay.
To monitor the voltage on
gap_junction_2 and record the spike, a probe can be set up
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. They are a fundamental building block in Arbor simulations. 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.
For single, stand-alone cable cells simulations, the Python API provides
single_cell_model that abstracts away the details of a recipe.
This is possible because the number of cells is fixed and known,
and it is guaranteed that there can be no connections or gap junctions in a model of 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.
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-centred 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
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.
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.
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:
minimising the memory footprint of the recipe.
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