Merge pull request #842 from apdavison/update-arbor

Update the Arbor backend to support Arbor 0.10.0 – 0.12.2

Author: apdavison

.github/workflows/full-test.yml

@@ -65,7 +65,7 @@ jobs:
     - name: Install Arbor
       if: startsWith(matrix.os, 'ubuntu')
       run: |
-        python -m pip install arbor==0.9.0 libNeuroML morphio
+        python -m pip install arbor==0.10.0 libNeuroML morphio
     - name: Install NESTML
       if: startsWith(matrix.os, 'ubuntu')
       run: |
@@ -102,6 +102,35 @@ jobs:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         COVERALLS_FLAG_NAME: ${{ matrix.test-name }}
         COVERALLS_PARALLEL: true
+  arbor-versions:
+    # Check that the Arbor backend works across the supported version range
+    name: Arbor ${{ matrix.arbor-version }} (Ubuntu, Python 3.12)
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        arbor-version: ["0.10.0", "0.12.2"]
+    steps:
+    - uses: actions/checkout@v6
+    - name: Set up Python 3.12
+      uses: actions/setup-python@v6
+      with:
+        python-version: "3.12"
+    - name: Install Arbor ${{ matrix.arbor-version }} and dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install pytest scipy
+        python -m pip install "arbor==${{ matrix.arbor-version }}" libNeuroML morphio
+    - name: Install PyNN itself
+      run: |
+        python -m pip install -e ".[test]"
+    - name: Test that the Arbor backend imports (builds the catalogue)
+      run: |
+        python -c "import arbor; print('arbor', arbor.__version__)"
+        python -c "import pyNN.arbor"
+    - name: Run Arbor unit and scenario tests
+      run: |
+        pytest -v test/unittests/test_arbor.py test/system/scenarios -k arbor
   coveralls:
     name: Indicate completion to coveralls.io
     needs: test

Makefile

@@ -97,7 +97,7 @@ $(NEST_STAMP): $(NEST_VENV_STAMP) $(NEST_SRC_UNPACKED)
 	    $(CURDIR)/pyNN/nest/extensions
 	cd $(NEST_BUILD_DIR)/pynn_extensions && make install
 	$(NEST_PIP) install \
-	    "neuron>=9.0.0" nrnutils "arbor==0.9.0" \
+	    "neuron>=9.0.0" nrnutils "arbor==0.10.0" \
 	    brian2 libNeuroML scipy matplotlib Cheetah3 h5py Jinja2 \
 	    pytest pytest-xdist pytest-cov flake8 morphio nestml
 	$(NEST_PIP) install -e .

pyNN/arbor/_compat.py

@@ -0,0 +1,84 @@
+"""
+Compatibility shims for supporting a range of Arbor versions from a single codebase.
+
+:copyright: Copyright 2006-2026 by the PyNN team, see AUTHORS.
+:license: CeCILL, see LICENSE for details.
+"""
+
+import arbor
+from arbor import units as U
+
+
+# --- iclamp was renamed to i_clamp in Arbor 0.12 -----------------------------
+
+_MECHANISM_RENAMES = {"iclamp": "i_clamp"}
+
+
+def get_electrode_mechanism(name):
+    """Return the Arbor current-source mechanism class for the given PyNN model
+    name, accounting for renames across Arbor versions (e.g. iclamp -> i_clamp)."""
+    for candidate in (name, _MECHANISM_RENAMES.get(name)):
+        if candidate is not None and hasattr(arbor, candidate):
+            return getattr(arbor, candidate)
+    raise AttributeError(f"Arbor has no current-source mechanism {name!r}")
+
+
+# --- decor.place() dropped the label arg for current stimuli in Arbor 0.12 ----
+
+def _current_stimulus_place_takes_label():
+    decor = arbor.decor()
+    clamp = get_electrode_mechanism("iclamp")(0.0 * U.ms, 0.0 * U.ms, 0.0 * U.nA)
+    try:
+        decor.place("(root)", clamp, "_probe")
+        return True
+    except TypeError:
+        return False
+
+
+_CURRENT_PLACE_TAKES_LABEL = _current_stimulus_place_takes_label()
+
+
+def place_current_source(decor, locset, mechanism, label):
+    """Place a current-clamp stimulus on a decor. Arbor 0.12 dropped the label
+    argument from the current-stimulus ``place()`` overload that 0.10 accepted
+    (synapse/junction/detector placements keep their label in all versions)."""
+    if _CURRENT_PLACE_TAKES_LABEL:
+        decor.place(locset, mechanism, label)
+    else:
+        decor.place(locset, mechanism)
+
+
+# --- cv_policy_max_extent gained units in Arbor 0.12 -------------------------
+
+def _cv_policy_wants_units():
+    try:
+        arbor.cv_policy_max_extent(1.0 * U.um)
+        return True
+    except TypeError:
+        return False
+
+
+_CV_POLICY_WANTS_UNITS = _cv_policy_wants_units()
+
+
+def max_extent_policy(length_um):
+    """Build a max-extent cv_policy from a length in micrometres, handling the
+    0.12 change that requires a unit-typed quantity."""
+    if _CV_POLICY_WANTS_UNITS:
+        return arbor.cv_policy_max_extent(length_um * U.um)
+    return arbor.cv_policy_max_extent(length_um)
+
+
+# --- discretisation moved from decor to cable_cell in Arbor 0.11 -------------
+
+_DECOR_HAS_DISCRETIZATION = hasattr(arbor.decor(), "discretization")
+
+
+def make_cable_cell(tree, decor, labels, discretization):
+    """Construct an ``arbor.cable_cell``, applying the discretisation cv_policy in
+    the way the installed Arbor version expects: as a ``decor`` method in 0.10, or
+    as a ``cable_cell`` constructor argument in 0.11+."""
+    if _DECOR_HAS_DISCRETIZATION:
+        decor.discretization(discretization)
+        return arbor.cable_cell(tree, decor, labels)
+    return arbor.cable_cell(tree, decor, labels, discretization=discretization)

pyNN/arbor/cells.py

@@ -2,12 +2,22 @@
 from collections import defaultdict
 from lazyarray import larray
 import arbor
+from arbor import units as U
 from neuroml import Point3DWithDiam
+from . import _compat
 from ..morphology import Morphology, NeuroMLMorphology, MorphIOMorphology, IonChannelDistribution
 from ..models import BaseCellType
 from ..parameters import ParameterSpace
 
 
+# Units for the parameters of current-source mechanisms (e.g. iclamp).
+ELECTRODE_PARAM_UNITS = {
+    "tstart": U.ms,
+    "duration": U.ms,
+    "current": U.nA,
+}
+
+
 def convert_point(p3d: Point3DWithDiam) -> arbor.mpoint:
     return arbor.mpoint(p3d.x, p3d.y, p3d.z, p3d.diameter/2)
 
@@ -64,7 +74,7 @@ def _build_tree(self, i):
                 if segment.name not in self.labels:
                     self.labels[segment.name] = f"(segment {i})"
         elif isinstance(std_morphology, MorphIOMorphology):
-            tree = arbor.load_swc_neuron(std_morphology.morphology_file, raw=True)
+            tree = arbor.load_swc_neuron(std_morphology.morphology_file).segment_tree
         else:
             raise ValueError("{} not supported as a neuron morphology".format(type(std_morphology)))
 
@@ -89,18 +99,22 @@ def _build_decor(self, i):
         decor = arbor.decor()
         # Set the default properties of the cell (this overrides the model defaults).
         decor.set_property(
-            cm=self.parameters["cm"][i] * 0.01,  # µF/cm² -> F/m²
-            rL=self.parameters["Ra"][i] * 1,     # Ω·cm
-            Vm=self.initial_values["v"][i]
+            cm=self.parameters["cm"][i] * U.uF / U.cm2,
+            rL=self.parameters["Ra"][i] * U.Ohm * U.cm,
+            Vm=self.initial_values["v"][i] * U.mV
         )
         if not self.parameters["ionic_species"]._evaluated:
             self.parameters["ionic_species"].evaluate(simplify=True)
         for ion_name, ionic_species in self.parameters["ionic_species"].items():
             assert ion_name == ionic_species.ion_name
-            decor.set_ion(ion_name,
-                          int_con=ionic_species.internal_concentration,
-                          ext_con=ionic_species.external_concentration,
-                          rev_pot=ionic_species.reversal_potential)  # method="nernst/na")
+            ion_kwargs = {}
+            if ionic_species.internal_concentration is not None:
+                ion_kwargs["int_con"] = ionic_species.internal_concentration * U.mM
+            if ionic_species.external_concentration is not None:
+                ion_kwargs["ext_con"] = ionic_species.external_concentration * U.mM
+            if ionic_species.reversal_potential is not None:
+                ion_kwargs["rev_pot"] = ionic_species.reversal_potential * U.mV
+            decor.set_ion(ion_name, **ion_kwargs)  # method="nernst/na")
         for native_name, region_params in mechanism_parameters.items():
             for region, params in region_params.items():
                 if native_name == "hh":
@@ -122,20 +136,20 @@ def _build_decor(self, i):
         # insert current sources
         for current_source in self.current_sources[i]:
             location_generator = current_source["location_generator"]
-            mechanism = getattr(arbor, current_source["model_name"])
+            mechanism = _compat.get_electrode_mechanism(current_source["model_name"])
             for locset, label in location_generator.generate_locations(morph, label=f"{current_source['model_name']}_label"):
-                params = current_source["parameters"].evaluate(simplify=True)
+                params = current_source["parameters"].evaluate(simplify=True).as_dict()
+                params = {
+                    name: value * ELECTRODE_PARAM_UNITS[name] if name in ELECTRODE_PARAM_UNITS else value
+                    for name, value in params.items()
+                }
                 mech = mechanism(**params)
-                decor.place(locset, mech, label)
+                _compat.place_current_source(decor, locset, mech, label)
 
         # add spike source
-        decor.place('"root"', arbor.threshold_detector(-10), "detector")
+        decor.place('"root"', arbor.threshold_detector(-10 * U.mV), "detector")
         # todo: allow user to choose location and threshold value
 
-        policy = arbor.cv_policy_max_extent(10.0)
-        # to do: allow user to specify this value and/or the policy more generally
-        decor.discretization(policy)
-
         return decor
 
     def set_initial_values(self, variable, initial_values):
@@ -158,10 +172,14 @@ def set_shape(self, value):
             pse.parameter_space.shape = value
 
     def __call__(self, i):
+        # The discretisation cv_policy is applied by _compat.make_cable_cell at
+        # cell-construction time (on the decor in Arbor 0.10, or as a cable_cell
+        # argument in 0.11+). to do: allow the user to specify this value/policy.
         return {
             "tree": self._build_tree(i),
             "decor": self._build_decor(i),
-            "labels": arbor.label_dict(self.labels)
+            "labels": arbor.label_dict(self.labels),
+            "discretization": _compat.max_extent_policy(10.0)
         }
 
 

pyNN/arbor/morphology.py

@@ -89,9 +89,12 @@ def generate_locations(self, morphology, label):
         for location in self.labels:
             if location == "soma":
                 # todo: proper location mapping
-                locsets.append(('"root"', f"{label}-{location}"))
+                # Use the resolved locset expression rather than a label reference
+                # ('"root"'): Arbor 0.12's label resolution does not resolve label
+                # references in probe locsets against the cell's label_dict.
+                locsets.append(('(root)', f"{label}-{location}"))
             elif location == "dendrite":
-                locsets.append(('"mid-dend"', f"{label}-{location}"))
+                locsets.append(('(location 0 0.5)', f"{label}-{location}"))
             elif isinstance(location, str):
                 locsets.append((
                     f'(on-components 0.5 (region "{location}"))',

pyNN/arbor/populations.py

@@ -10,6 +10,7 @@
 from ..standardmodels import StandardCellType
 from ..parameters import ParameterSpace, simplify, Sequence
 from . import simulator
+from . import _compat
 from .recording import Recorder
 
 
@@ -87,11 +88,22 @@ def arbor_cell_description(self, gid):
             for key, value in params.items():
                 if isinstance(value, Sequence):
                     params[key] = value.value
-            schedule = self.celltype.arbor_schedule(**params)
+            schedule_units = getattr(self.celltype, "arbor_schedule_units", {})
+            schedule_params = {}
+            for key, value in params.items():
+                unit = schedule_units.get(key)
+                if unit is None or value is None:
+                    schedule_params[key] = value
+                elif isinstance(value, (list, tuple, np.ndarray)):
+                    schedule_params[key] = [float(v) * unit for v in value]
+                else:
+                    schedule_params[key] = value * unit
+            schedule = self.celltype.arbor_schedule(**schedule_params)
             return arbor.spike_source_cell("spike-source", schedule)
         else:
             args = self._arbor_cell_description[index]
-            return arbor.cable_cell(args["tree"], args["decor"], args["labels"])
+            return _compat.make_cable_cell(
+                args["tree"], args["decor"], args["labels"], args["discretization"])
 
     def _create_cells(self):
         # for now, we create all cells and store them in memory,

pyNN/arbor/projections.py

@@ -6,6 +6,7 @@
 from itertools import repeat
 
 import arbor
+from arbor import units as U
 
 from .. import common
 from ..core import ezip
@@ -89,7 +90,7 @@ def arbor_connections(self, gid):
                                 (self.pre[cg.presynaptic_index], source),
                                 target,
                                 cg.weight,
-                                cg.delay
+                                cg.delay * U.ms
                             )
                         )
                 else:

pyNN/arbor/recording.py

@@ -5,6 +5,7 @@
 from collections import defaultdict
 import numpy as np
 import arbor
+from arbor import units as U
 
 from .. import recording
 from . import simulator
@@ -58,18 +59,24 @@ def _localize_variables(self, variables, locations):
         return resolved_variables
 
     def _set_arbor_sim(self, arbor_sim):
+        # Since Arbor 0.10.0, probes are addressed by (gid, tag) rather than by
+        # a positional cell_member index. The tag assigned here (a per-gid probe
+        # counter, in the same iteration order as _get_arbor_probes) must match
+        # the tag given to the corresponding probe there.
         self.handles = defaultdict(list)
         probe_indices = defaultdict(int)
         for variable in self.recorded:
             if variable.name != "spikes":
                 for cell in self.recorded[variable]:
-                    probeset_id = arbor.cell_member(cell.gid, probe_indices[cell.gid])
+                    tag = str(probe_indices[cell.gid])
                     probe_indices[cell.gid] += 1
-                    handle = arbor_sim.sample(probeset_id, arbor.regular_schedule(self.sampling_interval))
+                    handle = arbor_sim.sample(
+                        cell.gid, tag, arbor.regular_schedule(self.sampling_interval * U.ms))
                     self.handles[variable].append(handle)
 
     def _get_arbor_probes(self, gid):
         probes = []
+        probe_index = 0
         for variable in self.recorded:
             if variable.location is None:
                 pass
@@ -79,12 +86,15 @@ def _get_arbor_probes(self, gid):
             if gid in [cell.gid for cell in self.recorded[variable]]:
                 if variable.name == "spikes":
                     continue
-                elif variable.name == "v":
-                    probe = arbor.cable_probe_membrane_voltage(locset)
+                # Tag must match the one assigned in _set_arbor_sim (per-gid index).
+                tag = str(probe_index)
+                probe_index += 1
+                if variable.name == "v":
+                    probe = arbor.cable_probe_membrane_voltage(locset, tag)
                 else:
                     mech_name, state_name = variable.name.split(".")
                     arbor_model = mech_name  # to do: find_arbor_model(mech_name)
-                    probe = arbor.cable_probe_density_state(locset, arbor_model, state_name)
+                    probe = arbor.cable_probe_density_state(locset, arbor_model, state_name, tag)
                 probes.append(probe)
         return probes