A simple single cell recipe

This example builds the same single cell model as the previous tutorial, except using a arbor.recipe and arbor.simulation instead of a arbor.single_cell_model.

Note

Concepts covered in this example:

  1. Building a arbor.recipe.

  2. Using the recipe, default context and domain decomposition to create an arbor.simulation

  3. Running the simulation and visualizing the results.

The cell

Let’s copy the cell description from the previous example, where construction of the cell is explained in detail.

import arbor

# (1) Create a morphology with a single (cylindrical) segment of length=diameter=6 μm
tree = arbor.segment_tree()
tree.append(arbor.mnpos, arbor.mpoint(-3, 0, 0, 3), arbor.mpoint(3, 0, 0, 3), tag=1)

# (2) Define the soma and its midpoint
labels = arbor.label_dict({'soma':   '(tag 1)',
                           'midpoint': '(location 0 0.5)'})

# (3) Create cell and set properties
decor = arbor.decor()
decor.set_property(Vm=-40)
decor.paint('"soma"', 'hh')
decor.place('"midpoint"', arbor.iclamp( 10, 2, 0.8), "iclamp")
decor.place('"midpoint"', arbor.spike_detector(-10), "detector")
cell = arbor.cable_cell(tree, labels, decor)

The recipe

In the previous example, the arbor.single_cell_model creates a arbor.recipe under the hood, and abstracts away a few details that you may want control over in more complex simulations. Let’s go into those abstractions and create an analogous arbor.recipe manually.

Creating a recipe starts with creating a class that inherits from arbor.recipe and overrides and implements some of arbor.recipe methods. Not all methods have to be overridden, but some will always have to be, such as arbor.recipe.num_cells(). It returns 0 by default and models without cells are quite boring!

# (4) Define a recipe for a single cell and set of probes upon it.
# This constitutes the corresponding generic recipe version of
# `single_cell_model.py`.

class single_recipe (arbor.recipe):
    def __init__(self, cell, probes):
        # (4.1) The base C++ class constructor must be called first, to ensure that
        # all memory in the C++ class is initialized correctly.
        arbor.recipe.__init__(self)
        self.the_cell = cell
        self.the_probes = probes
        self.the_props = arbor.neuron_cable_properties()
        self.the_cat = arbor.default_catalogue()
        self.the_props.register(self.the_cat)

    def num_cells(self):
        # (4.2) Override the num_cells method
        return 1

    def cell_kind(self, gid):
        # (4.3) Override the cell_kind method
        return arbor.cell_kind.cable

    def cell_description(self, gid):
        # (4.4) Override the cell_description method
        return self.the_cell

    def probes(self, gid):
        # (4.5) Override the probes method
        return self.the_probes

    def global_properties(self, kind):
        # (4.6) Override the global_properties method
        return self.the_props

# (5) Instantiate recipe with a voltage probe located on "midpoint".

recipe = single_recipe(cell, [arbor.cable_probe_membrane_voltage('"midpoint"')])

Step (4) describes the recipe that will reflect our single cell model.

Step (4.1) defines the class constructor. It can take any shape you need, but it is important to call base class’ constructor. If the overridden methods of the class need to return an object, it may be a good idea to have the returned object be a member of the class. With this constructor, we could easily change the cell and probes of the model, should we want to do so. Here we initialize the cell properties to match Neuron’s defaults using Arbor’s built-in arbor.neuron_cable_properties() and extend with Arbor’s own arbor.default_catalogue().

Step (4.2) defines that this model has one cell.

Step (4.3) returns arbor.cell_kind.cable, the arbor.cell_kind associated with the cable cell defined above. If you mix multiple cell kinds and descriptions in one recipe, make sure a particular gid returns matching cell kinds and descriptions.

Step (4.4) returns the cell description passed in on class initialisation. If we were modelling multiple cells of different kinds, we would need to make sure that the cell returned by arbor.recipe.cell_description() has the same cell kind as returned by arbor.recipe.cell_kind() for every gid.

Step (4.5) returns the probes passed in at class initialisation.

Step (4.6) returns the properties that will be applied to all cells of that kind in the model.

More methods can be overridden if your model requires that, see arbor.recipe for options.

Step (5) instantiates the recipe with the cable cell described earlier, and a single voltage probe located at “midpoint”.

The context and domain decomposition

arbor.single_cell_model does not only take care of the recipe, it also takes care of defining how the simulation will be run. When you create and use your own recipe, you’ll need to do this manually, in the form of defining a execution context and a domain decomposition. Fortunately, the default constructors of arbor.context and arbor.partition_load_balance are sufficient for this model, and is what arbor.single_cell_model does under the hood! We’ll leave the details of this subject for another tutorial.

# (6) Create a default execution context and a default domain decomposition.

context = arbor.context()
domains = arbor.partition_load_balance(recipe, context)

Step (6) sets up a default context and domains.

The simulation

# (7) Create and run simulation and set up 10 kHz (every 0.1 ms) sampling on the probe.
# The probe is located on cell 0, and is the 0th probe on that cell, thus has probe_id (0, 0).

sim = arbor.simulation(recipe, domains, context)
sim.record(arbor.spike_recording.all)
handle = sim.sample((0, 0), arbor.regular_schedule(0.1))
sim.run(tfinal=30)

Step (7) instantiates the simulation and sets up the probe added in step 5. In the arbor.single_cell_model version of this example, the probe frequency and simulation duration are the same. Note that the frequency is set with a arbor.regular_schedule, which takes a time and not a frequency. Also note that spike recording must be switched on. For extraction of the probe traces later on, we store a handle.

The results

Apart from creating arbor.recipe ourselves, we have changed nothing about this simulation compared to the previous tutorial. If we create the same analysis of the results we therefore expect the same results.

# (8) Collect results.

spikes = sim.spikes()
data, meta = sim.samples(handle)[0]

if len(spikes)>0:
    print('{} spikes:'.format(len(spikes)))
    for t in spikes['time']:
        print('{:3.3f}'.format(t))
else:
    print('no spikes')

print("Plotting results ...")

df = pandas.DataFrame({'t/ms': data[:, 0], 'U/mV': data[:, 1]})
seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV", ci=None).savefig('single_cell_recipe_result.svg')

df.to_csv('single_cell_recipe_result.csv', float_format='%g')

Step (8) plots the measured potentials during the runtime of the simulation. Retrieving the sampled quantities is a little different, these have to be accessed through the simulation object: arbor.simulation.spikes() and arbor.simulation.samples().

We should be seeing something like this:

../_images/single_cell_model_result.svg

Plot of the potential over time for the voltage probe added in step (5).

You can find the source code for this example in full at python/examples/single_cell_recipe.py.