Ex25: Multi-Compartment Cells

Cells with multiple dendritic compartments connected to a network

Interoperability·NeuroML / LEMS

Model: Multi-Compartment Network

Three multi-compartment HH cells (4 segments each: soma + 3 dendrites) connected via AMPA and NMDA synapses with segment-targeted connections. Pulse generator inputs drive cells 0 and 2; cell 1 receives synaptic input.

Reference: NeuroML2 LEMS_NML2_Ex25_MultiComp.xml

---

1. Define Network in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex25: MultiComp"
network:
  dynamics:
    # ── Ion Channels (imported from NML2_SingleCompHHCell.nml) ──
    passiveChan:
      name: passiveChan
      iri: neuroml:ionChannelHH
      parameters:
        conductance: {value: 10, unit: pS}
        condDensity: {value: 3.0, unit: S_per_m2}
        erev: {value: -54.3, unit: mV}
        ion: {label: non_specific}
    naChan:
      name: naChan
      iri: neuroml:ionChannelHH
      parameters:
        conductance: {value: 10, unit: pS}
        species: {label: na}
        condDensity: {value: 120.0, unit: mS_per_cm2}
        erev: {value: 50.0, unit: mV}
        ion: {label: na}
      modes:
        m:
          name: m
          iri: neuroml:gateHHrates
          parameters:
            instances: {value: 3}
          modes:
            forwardRate:
              name: forwardRate
              iri: neuroml:HHExpLinearRate
              parameters:
                rate: {value: 1, unit: per_ms}
                midpoint: {value: -40, unit: mV}
                scale: {value: 10, unit: mV}
            reverseRate:
              name: reverseRate
              iri: neuroml:HHExpRate
              parameters:
                rate: {value: 4, unit: per_ms}
                midpoint: {value: -65, unit: mV}
                scale: {value: -18, unit: mV}
        h:
          name: h
          iri: neuroml:gateHHrates
          parameters:
            instances: {value: 1}
          modes:
            forwardRate:
              name: forwardRate
              iri: neuroml:HHExpRate
              parameters:
                rate: {value: 0.07, unit: per_ms}
                midpoint: {value: -65, unit: mV}
                scale: {value: -20, unit: mV}
            reverseRate:
              name: reverseRate
              iri: neuroml:HHSigmoidRate
              parameters:
                rate: {value: 1, unit: per_ms}
                midpoint: {value: -35, unit: mV}
                scale: {value: 10, unit: mV}
    kChan:
      name: kChan
      iri: neuroml:ionChannelHH
      parameters:
        conductance: {value: 10, unit: pS}
        species: {label: k}
        condDensity: {value: 360, unit: S_per_m2}
        erev: {value: -77, unit: mV}
        ion: {label: k}
      modes:
        n:
          name: n
          iri: neuroml:gateHHrates
          parameters:
            instances: {value: 4}
          modes:
            forwardRate:
              name: forwardRate
              iri: neuroml:HHExpLinearRate
              parameters:
                rate: {value: 0.1, unit: per_ms}
                midpoint: {value: -55, unit: mV}
                scale: {value: 10, unit: mV}
            reverseRate:
              name: reverseRate
              iri: neuroml:HHExpRate
              parameters:
                rate: {value: 0.125, unit: per_ms}
                midpoint: {value: -65, unit: mV}
                scale: {value: -80, unit: mV}

    # ── Multi-Compartment Cell ──
    MultiCompCell:
      name: MultiCompCell
      iri: neuroml:cell
      parameters:
        specificCapacitance: {value: 1.0, unit: uF_per_cm2}
        initMembPotential: {value: -65, unit: mV}
        spikeThresh: {value: -20, unit: mV}
        resistivity: {value: 100, unit: kohm_cm}
      modes:
        # Channels (shared across all segments via channelDensity)
        passiveChan:
          name: passiveChan
          iri: neuroml:ionChannelHH
          parameters:
            conductance: {value: 10, unit: pS}
            condDensity: {value: 3.0, unit: S_per_m2}
            erev: {value: -54.3, unit: mV}
            ion: {label: non_specific}
        naChan:
          name: naChan
          iri: neuroml:ionChannelHH
          parameters:
            conductance: {value: 10, unit: pS}
            species: {label: na}
            condDensity: {value: 120.0, unit: mS_per_cm2}
            erev: {value: 50.0, unit: mV}
            ion: {label: na}
          modes:
            m:
              name: m
              iri: neuroml:gateHHrates
              parameters:
                instances: {value: 3}
              modes:
                forwardRate:
                  name: forwardRate
                  iri: neuroml:HHExpLinearRate
                  parameters:
                    rate: {value: 1, unit: per_ms}
                    midpoint: {value: -40, unit: mV}
                    scale: {value: 10, unit: mV}
                reverseRate:
                  name: reverseRate
                  iri: neuroml:HHExpRate
                  parameters:
                    rate: {value: 4, unit: per_ms}
                    midpoint: {value: -65, unit: mV}
                    scale: {value: -18, unit: mV}
            h:
              name: h
              iri: neuroml:gateHHrates
              parameters:
                instances: {value: 1}
              modes:
                forwardRate:
                  name: forwardRate
                  iri: neuroml:HHExpRate
                  parameters:
                    rate: {value: 0.07, unit: per_ms}
                    midpoint: {value: -65, unit: mV}
                    scale: {value: -20, unit: mV}
                reverseRate:
                  name: reverseRate
                  iri: neuroml:HHSigmoidRate
                  parameters:
                    rate: {value: 1, unit: per_ms}
                    midpoint: {value: -35, unit: mV}
                    scale: {value: 10, unit: mV}
        kChan:
          name: kChan
          iri: neuroml:ionChannelHH
          parameters:
            conductance: {value: 10, unit: pS}
            species: {label: k}
            condDensity: {value: 360, unit: S_per_m2}
            erev: {value: -77, unit: mV}
            ion: {label: k}
          modes:
            n:
              name: n
              iri: neuroml:gateHHrates
              parameters:
                instances: {value: 4}
              modes:
                forwardRate:
                  name: forwardRate
                  iri: neuroml:HHExpLinearRate
                  parameters:
                    rate: {value: 0.1, unit: per_ms}
                    midpoint: {value: -55, unit: mV}
                    scale: {value: 10, unit: mV}
                reverseRate:
                  name: reverseRate
                  iri: neuroml:HHExpRate
                  parameters:
                    rate: {value: 0.125, unit: per_ms}
                    midpoint: {value: -65, unit: mV}
                    scale: {value: -80, unit: mV}

        # ── Morphology: segments ──
        Soma:
          name: Soma
          iri: neuroml:segment
          parameters:
            id: {value: 0}
            proximal_x: {value: 0}
            proximal_y: {value: 0}
            proximal_z: {value: 0}
            proximal_diameter: {value: 10}
            distal_x: {value: 0}
            distal_y: {value: 10}
            distal_z: {value: 0}
            distal_diameter: {value: 10}
        Dendrite1:
          name: Dendrite1
          iri: neuroml:segment
          parameters:
            id: {value: 1}
            parent: {value: 0}
            proximal_x: {value: 0}
            proximal_y: {value: 10}
            proximal_z: {value: 0}
            proximal_diameter: {value: 3}
            distal_x: {value: 0}
            distal_y: {value: 20}
            distal_z: {value: 0}
            distal_diameter: {value: 3}
        Dendrite2a:
          name: Dendrite2a
          iri: neuroml:segment
          parameters:
            id: {value: 2}
            parent: {value: 1}
            proximal_x: {value: 0}
            proximal_y: {value: 20}
            proximal_z: {value: 0}
            proximal_diameter: {value: 3}
            distal_x: {value: 0}
            distal_y: {value: 30}
            distal_z: {value: 0}
            distal_diameter: {value: 2.5}
        Dendrite2b:
          name: Dendrite2b
          iri: neuroml:segment
          parameters:
            id: {value: 3}
            parent: {value: 2}
            distal_x: {value: 0}
            distal_y: {value: 50}
            distal_z: {value: 0}
            distal_diameter: {value: 1.5}

        # ── Morphology: segment groups ──
        soma:
          name: soma
          iri: neuroml:segmentGroup
          parameters:
            neuroLexId: {label: sao864921383}
            members: {shape: "0"}
        dendSec1:
          name: dendSec1
          iri: neuroml:segmentGroup
          parameters:
            neuroLexId: {label: sao864921383}
            members: {shape: "1"}
        dendSec2:
          name: dendSec2
          iri: neuroml:segmentGroup
          parameters:
            neuroLexId: {label: sao864921383}
            numberInternalDivisions: {value: 9}
            members: {shape: "2,3"}
        soma_group:
          name: soma_group
          iri: neuroml:segmentGroup
          parameters:
            includes: {shape: soma}
        dendrite_group:
          name: dendrite_group
          iri: neuroml:segmentGroup
          parameters:
            includes: {shape: "dendSec1,dendSec2"}

    # ── Synapses ──
    AMPA:
      name: AMPA
      iri: neuroml:expTwoSynapse
      parameters:
        tauRise: {value: 3e-5, unit: s}
        tauDecay: {value: 0.5e-3, unit: s}
        gbase: {value: 0.3, unit: nS}
        erev: {value: 0, unit: V}
    NMDA:
      name: NMDA
      iri: neuroml:blockingPlasticSynapse
      parameters:
        gbase: {value: 0.8, unit: nS}
        tauRise: {value: 1e-3, unit: s}
        tauDecay: {value: 13.3333e-3, unit: s}
        erev: {value: 0, unit: V}
      modes:
        blockMechanism:
          name: blockMechanism
          iri: neuroml:voltageConcDepBlockMechanism
          parameters:
            species: {label: mg}
            blockConcentration: {value: 1.2, unit: mM}
            scalingConc: {value: 1.9205441817997078, unit: mM}
            scalingVolt: {value: 0.016129032258064516, unit: V}

    # ── Inputs ──
    pulseGen2:
      name: pulseGen2
      iri: neuroml:pulseGenerator
      parameters:
        delay: {value: 20, unit: ms}
        duration: {value: 100, unit: ms}
        amplitude: {value: 0.2, unit: nA}
    pulseGen3:
      name: pulseGen3
      iri: neuroml:pulseGenerator
      parameters:
        delay: {value: 30, unit: ms}
        duration: {value: 100, unit: ms}
        amplitude: {value: 0.18, unit: nA}

  # ── Network topology ──
  nodes:
    # 3 multi-compartment cells
    - id: 0
      dynamics: MultiCompCell
      position: {x: 0, y: 0, z: 0}
    - id: 1
      dynamics: MultiCompCell
      position: {x: 30, y: 0, z: 0}
    - id: 2
      dynamics: MultiCompCell
      position: {x: 60, y: 0, z: 0}
    # Input sources
    - id: 100
      dynamics: pulseGen2
    - id: 101
      dynamics: pulseGen3

  edges:
    # ── Inputs to cells ──
    - source: 100
      target: 0
      parameters:
        segmentId: {value: 0}
        fractionAlong: {value: 0.5}
    - source: 101
      target: 2
      parameters:
        segmentId: {value: 0}
        fractionAlong: {value: 0.5}

    # ── AMPA projections: cells 0,2 → cell 1 ──
    - source: 0
      target: 1
      coupling: AMPA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 0}
        postFractionAlong: {value: 0.5}
    - source: 0
      target: 1
      coupling: AMPA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 3}
        postFractionAlong: {value: 0.3}
    - source: 2
      target: 1
      coupling: AMPA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 0}
        postFractionAlong: {value: 0.5}
    - source: 2
      target: 1
      coupling: AMPA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 1}
        postFractionAlong: {value: 0.5}
    - source: 2
      target: 1
      coupling: AMPA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 3}
        postFractionAlong: {value: 0.25}

    # ── NMDA projections: cells 0,2 → cell 1 ──
    - source: 0
      target: 1
      coupling: NMDA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 0}
        postFractionAlong: {value: 0.5}
    - source: 0
      target: 1
      coupling: NMDA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 3}
        postFractionAlong: {value: 0.5}
    - source: 2
      target: 1
      coupling: NMDA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 0}
        postFractionAlong: {value: 0.5}
    - source: 2
      target: 1
      coupling: NMDA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 1}
        postFractionAlong: {value: 0.5}
    - source: 2
      target: 1
      coupling: NMDA
      parameters:
        preSegmentId: {value: 0}
        preFractionAlong: {value: 0.5}
        postSegmentId: {value: 3}
        postFractionAlong: {value: 0.25}

integration:
  method: euler
  step_size: 0.005
  duration: 140.0
  time_scale: ms
""")
print(f"Model: {exp.dynamics.name if exp.dynamics else 'network'}")

Model: network

* Owlready2 * Warning: ignoring cyclic subclass of/subproperty of, involving:
  http://uri.interlex.org/tgbugs/uris/readable/atlas/Space

2. Render LEMS XML

xml = exp.render("lems")
print(xml[:2000])

<Lems>

  <!-- Tell jLEMS/jNeuroML which component is the simulation entry point. -->
  <Target component="sim_NeuroML_Ex25__MultiComp"/>

  <!-- ════════════════════════════════════════════════════════════════
       Standard NeuroML2 type includes for network mode.
       Provides all standard dimensions, units, synapse types, input
       types, network infrastructure, and simulation types.
       ════════════════════════════════════════════════════════════════ -->
  <Include file="Cells.xml"/>
  <Include file="Networks.xml"/>
  <Include file="Simulation.xml"/>

  <!-- ════════════════════════════════════════════════════════════════
       Dynamics ComponentType & Component instances
       ════════════════════════════════════════════════════════════════ -->


  <!-- ── ComponentType: MultiCompCell ── -->
  <ComponentType name="MultiCompCell" extends="baseCellMembPot">
    <Parameter name="initMembPotential" dimension="voltage"/>
    <Parameter name="resistivity" dimension="none"/>
    <Parameter name="specificCapacitance" dimension="none"/>
    <Parameter name="spikeThresh" dimension="voltage"/>
    <Constant name="SEC" dimension="time" value="1ms"/>
    <!-- Dynamically attached synapses/inputs from network connections -->
    <Attachments name="synapses" type="basePointCurrent"/>

    <Dynamics>
      <!-- ── Flat dynamics ── -->
      <OnStart>
      </OnStart>
    </Dynamics>
  </ComponentType>

  <Component id="MultiCompCell_inst" type="MultiCompCell" initMembPotential="-65 mV" resistivity="100" specificCapacitance="1.0" spikeThresh="-20 mV"/>


  <!-- ════════════════════════════════════════════════════════════════
       Built-in/named synapse definitions (no custom ODE dynamics)
       ════════════════════════════════════════════════════════════════ -->
  <!-- Synapse: AMPA (AMPA) -->
  <AMPA id="AMPA" postFractionAlong="0.5" postSegmentId="0" preFractionAlong="0.5" preSegmentId="0"/>
  <!-- Synapse: AMPA_2 (AMPA) -->
  <AMPA id="AMPA_2" postFractionAlon

/Users/leonmartin_bih/tools/tvbo/tvbo/adapters/neuroml.py:2787: UserWarning: synapse 'AMPA' is used with more than one set of parameters; emitting the additional one as 'AMPA_2'. Give each parameterisation its own entry in the dynamics library to choose the names yourself.
  syn_id = _unique_component_id(syn_type, assigned_syn_ids, kind="synapse")
/Users/leonmartin_bih/tools/tvbo/tvbo/adapters/neuroml.py:2787: UserWarning: synapse 'AMPA' is used with more than one set of parameters; emitting the additional one as 'AMPA_3'. Give each parameterisation its own entry in the dynamics library to choose the names yourself.
  syn_id = _unique_component_id(syn_type, assigned_syn_ids, kind="synapse")
/Users/leonmartin_bih/tools/tvbo/tvbo/adapters/neuroml.py:2787: UserWarning: synapse 'AMPA' is used with more than one set of parameters; emitting the additional one as 'AMPA_4'. Give each parameterisation its own entry in the dynamics library to choose the names yourself.
  syn_id = _unique_component_id(syn_type, assigned_syn_ids, kind="synapse")
/Users/leonmartin_bih/tools/tvbo/tvbo/adapters/neuroml.py:2787: UserWarning: synapse 'NMDA' is used with more than one set of parameters; emitting the additional one as 'NMDA_2'. Give each parameterisation its own entry in the dynamics library to choose the names yourself.
  syn_id = _unique_component_id(syn_type, assigned_syn_ids, kind="synapse")
/Users/leonmartin_bih/tools/tvbo/tvbo/adapters/neuroml.py:2787: UserWarning: synapse 'NMDA' is used with more than one set of parameters; emitting the additional one as 'NMDA_3'. Give each parameterisation its own entry in the dynamics library to choose the names yourself.
  syn_id = _unique_component_id(syn_type, assigned_syn_ids, kind="synapse")
/Users/leonmartin_bih/tools/tvbo/tvbo/adapters/neuroml.py:2787: UserWarning: synapse 'NMDA' is used with more than one set of parameters; emitting the additional one as 'NMDA_4'. Give each parameterisation its own entry in the dynamics library to choose the names yourself.
  syn_id = _unique_component_id(syn_type, assigned_syn_ids, kind="synapse")

3. Run Reference

from tvbo.adapters.neuroml import run_lems_example

ref_outputs = run_lems_example("LEMS_NML2_Ex25_MultiComp.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  ex25_0.dat: shape=(28001, 5)
  ex25_1.dat: shape=(28001, 5)
  ex25_2.dat: shape=(28001, 5)

4. Run TVBO

result = exp.run("neuroml", backend="neuron")
da = result.integration.data
print(f"TVBO: {da.dims}, shape={da.shape}")

pyNeuroML >>> 13:06:34 - INFO - Loading LEMS file: tvbo_lems_sim.xml and running with jNeuroML_NEURON
pyNeuroML >>> 13:06:34 - INFO - Executing: (java -Xmx400M  -Djava.awt.headless=true -jar  "/Users/leonmartin_bih/tools/tvbo/.venv/lib/python3.12/site-packages/pyneuroml/utils/./../lib/jNeuroML-0.14.0-jar-with-dependencies.jar"  tvbo_lems_sim.xml  -neuron -run -compile -nogui -I '') in directory: /var/folders/ym/9kw1g21j1nd7kwfn8c0z3st40000gn/T/tmp3ehxj87m
pyNeuroML >>> 13:06:37 - INFO - Command completed successfully!

TVBO: ('time', 'quantity'), shape=(28001, 12)

5. Compare & Plot

from tvbo.adapters.neuroml import plot_lems_comparison
plot_lems_comparison("LEMS_NML2_Ex25_MultiComp.xml", ref_outputs, result.integration.data, title_prefix="Ex25")