A detailed single cell recipe¶
This example builds the same single cell model as A detailed single cell model,
except using a arbor.recipe
and arbor.simulation
instead of a arbor.single_cell_model
.
This time, we’ll learn a bit more about setting up advanced features using a arbor.recipe
.
Note
Concepts covered in this example:
Building a
arbor.recipe
.Building an
arbor.context
.Create a
arbor.simulation
.Running the simulation and visualizing the results,
The cell¶
We can copy the cell description code or reuse single_cell_detailed.swc
from the
original example where it is explained in detail.
The recipe¶
The arbor.single_cell_model
of the original example created a arbor.recipe
under
the hood, and abstracted away the details so we were unaware of its existence. In this example, we will
examine the recipe in detail: how to create one, and why it is needed.
# (5) Create a class that inherits from arbor.recipe
class single_recipe(arbor.recipe):
# (5.1) Define the class constructor
def __init__(self):
# 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_props = arbor.cable_global_properties()
self.the_props.set_property(Vm=-65, tempK=300, rL=35.4, cm=0.01)
self.the_props.set_ion(
ion="na", int_con=10, ext_con=140, rev_pot=50, method="nernst/na"
)
self.the_props.set_ion(ion="k", int_con=54.4, ext_con=2.5, rev_pot=-77)
self.the_props.set_ion(ion="ca", int_con=5e-5, ext_con=2, rev_pot=132.5)
self.the_props.catalogue.extend(arbor.allen_catalogue(), "")
# (5.2) Override the num_cells method
def num_cells(self):
return 1
# (5.3) Override the cell_kind method
def cell_kind(self, _):
return arbor.cell_kind.cable
# (5.4) Override the cell_description method
def cell_description(self, _):
return cell
# (5.5) Override the probes method
def probes(self, _):
return [arbor.cable_probe_membrane_voltage('"custom_terminal"', "Um")]
# (5.6) Override the global_properties method
def global_properties(self, gid):
return self.the_props
Let’s go through the recipe point by point.
Step (5) creates a single_recipe
class that inherits from arbor.recipe
.
arbor.recipe.num_cells()
, arbor.recipe.cell_kind()
and arbor.recipe.cell_description()
always have to be implemented by the user. We’ll also implement arbor.recipe.global_properties()
to be able
to decorate arbor.cell_kind.cable
cells with mechanisms and arbor.recipe.probes()
to be able to
insert the probe.
Step (5.1) defines the class constructor. As per arbor.recipe
instructions, we call
arbor.recipe.__init__(self)
to ensure correct initialization of memory in the C++ class.
We then create the self.the_props
variable. This will hold the global properties of the model, which apply
to all the cells in the network. We initialize it with arbor.cable_global_properties
, which comes with the
default
mechanism catalogue built-in. We set all the properties of the system similar to what we did in
the original example. One last important step is to extend self.the_props
to include the Allen catalogue, because it holds the Ih mechanism. The hh and pas mechanisms came with the
default catalogue.
Step (5.2) overrides the num_cells()
method. It takes no arguments. We simply return 1,
as we are only simulating one cell in this example.
Step (5.3) overrides the cell_kind()
method. It takes one argument: gid
.
Given the gid, this method returns the kind of the cell. Our defined cell is a
arbor.cell_kind.cable
, so we simply return that.
Step (5.4) overrides the cell_description()
method. It takes one argument: gid
.
Given the gid, this method returns the cell description which is the cell object passed to the constructor
of the recipe. We return cell
, the cell created just above.
Step (5.5) overrides the probes()
method. It takes one argument: gid
.
Given the gid, this method returns all the probes on the cell. The probes can be of many different kinds
measuring different quantities on different locations of the cell. Like in the original example, we will
create the voltage probe at the "custom_terminal"
locset. This probe was registered directly using the
arbor.single_cell_model
object. Now it has to be explicitly created and registered in the recipe.
Step (5.6) overrides the global_properties()
method. It takes one argument: kind
.
This method returns the default global properties of the model which apply to all cells in the network of
that kind. We only use cable
cells in this example (but there are more) and thus always return a
cable_cell_properties
object. We return self.the_props
which we defined in step (1).
Note
You may wonder why the method arbor.recipe.cell_kind()
is required, since it can be inferred by examining the cell description.
The recipe was designed to allow building simulations efficiently in a distributed system with minimum
communication. Some parts of the model initialization require only the cell kind,
not the full cell description which can be quite expensive to build. Providing these
descriptions separately saves time and resources for the user.
More information on the recipe can be found here.
Now we can instantiate a single_recipe
object.
# Instantiate recipe
recipe = single_recipe()
The simulation¶
We have all we need to create a arbor.simulation
object.
Before we run the simulation, however, we need to register what results we expect once execution is over.
This was handled by the arbor.single_cell_model
object in the original example.
# (6) Create a simulation
sim = arbor.simulation(recipe)
# Instruct the simulation to record the spikes and sample the probe
sim.record(arbor.spike_recording.all)
handle = sim.sample((0, "Um"), arbor.regular_schedule(0.02))
We would like to get a list of the spikes on the cell during the runtime of the simulation, and we would like to plot the voltage registered by the probe on the “custom_terminal” locset.
The lines handling probe sampling warrant a second look. First, we declared probeset_id
to be a
arbor.cell_member
, with arbor.cell_member.gid
= 0 and arbor.cell_member.index
= 0.
This variable serves as a global identifier of a probe on a cell, namely the first declared probe on the
cell with gid = 0
, which is id of the only probe we created on the only cell in the model.
Next, we instructed the simulation to sample probeset_id
at a frequency of 50 kHz. That function returns a
handle which we will use to extract the results of the sampling after running the simulation.
We can now run the simulation we just instantiated for a duration of 100 ms with a time step of 0.025 ms.
sim.run(tfinal=100, dt=0.025)
The results¶
The last step is result collection. We instructed the simulation to record the spikes on the cell, and to sample the probe.
We can print the times of the spikes:
spikes = sim.spikes()
print(len(spikes), "spikes recorded:")
for s in spikes:
print(s)
The probe results, again, warrant some more explanation:
meta = []
for d, m in sim.samples(handle):
data.append(d)
meta.append(m)
sim.samples()
takes a handle
of the probe we wish to examine. It returns a list
of (data, meta)
terms: data
being the time and value series of the probed quantity; and
meta
being the location of the probe. The size of the returned list depends on the number of
discrete locations pointed to by the handle. We placed the probe on the “custom_terminal” locset which is
represented by 2 locations on the morphology. We therefore expect the length of sim.samples(handle)
to be 2.
We plot the results using pandas and seaborn as we did in the original example, and expect the same results:
for i in range(len(data)):
df_list.append(
pandas.DataFrame(
{
"t/ms": data[i][:, 0],
"U/mV": data[i][:, 1],
"Location": str(meta[i]),
"Variable": "voltage",
}
)
)
df = pandas.concat(df_list, ignore_index=True)
seaborn.relplot(
data=df,
kind="line",
x="t/ms",
y="U/mV",
hue="Location",
col="Variable",
errorbar=None,
).savefig("single_cell_recipe_result.svg")
The following plot is generated. Identical to the plot of the original example.
The full code¶
You can find the full code of the example at python/examples/single_cell_detailed_recipe.py
.