Ex25: Multi-Compartment Cells

Cells with multiple dendritic compartments connected to a network

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

2. Render LEMS XML

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

<Lems>
  <Target component="sim_NeuroML_Ex25__MultiComp"/>

  <Include file="Cells.xml"/>
  <Include file="Networks.xml"/>
  <Include file="Inputs.xml"/>
  <Include file="Simulation.xml"/>

    <ionChannelHH id="kChan" conductance="10 pS" species="k">
        <gateHHrates id="n" instances="4">
            <forwardRate type="HHExpLinearRate" midpoint="-55 mV" rate="0.1 per_ms" scale="10 mV"/>
            <reverseRate type="HHExpRate" midpoint="-65 mV" rate="0.125 per_ms" scale="-80 mV"/>
        </gateHHrates>
    </ionChannelHH>

    <ionChannelHH id="naChan" conductance="10 pS" species="na">
        <gateHHrates id="h" instances="1">
            <forwardRate type="HHExpRate" midpoint="-65 mV" rate="0.07 per_ms" scale="-20 mV"/>
            <reverseRate type="HHSigmoidRate" midpoint="-35 mV" rate="1 per_ms" scale="10 mV"/>
        </gateHHrates>
        <gateHHrates id="m" instances="3">
            <forwardRate type="HHExpLinearRate" midpoint="-40 mV" rate="1 per_ms" scale="10 mV"/>
            <reverseRate type="HHExpRate" midpoint="-65 mV" rate="4 per_ms" scale="-18 mV"/>
        </gateHHrates>
    </ionChannelHH>

    <ionChannelHH id="passiveChan" conductance="10 pS"/>

    <cell id="MultiCompCell">
        <morphology id="MultiCompCell_morphology">
            <segment id ="0" name="Soma">
                <proximal x="0.0" y="0.0" z="0.0" diameter="10.0"/>
                <distal x="0.0" y="10.0" z="0.0" diameter="10.0"/>
            </segment>
            <segment id ="1" name="Dendrite1">
                <parent segment="0"/>
                <proximal x="0.0" y="10.0" z="0.0" diameter="3.0"/>
                <distal x="0.0" y="20.0" z="0.0" diameter="3.0"/>
            </segment>
            <segment id ="2" name="Dendrite2a">
                <parent segment="1"/>
                <proximal x="0.0" y="20.0" z="0.0" diameter="3.0"/>
                <distal x="0.0" y="30.0" z="0.0" diameter="2.5"/>
            </segment>
            <segment id ="3" name="Dendr

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}")

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")