Ex26: Connection Weights

Weighted chemical, electrical, continuous connections and weighted inputs

Model: Weighted Connections

Demonstrates all weight-bearing connection types in NeuroML2:

• Chemical: <connection> (weight=1) and <connectionWD> (weight=0.5/2, delay=20/40ms)
• Electrical: <electricalConnectionInstanceW> (weight=1/0.5/2)
• Continuous: <continuousConnectionInstanceW> (weight=1/0.5/2)
• Inputs: <inputList>/<inputW> (weight=1/0.5/2)

3 pre-synaptic HH cells receive weighted current injection; 9 post-synaptic HH cells receive synaptic input via different projection types.

---

1. Define Network in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex26: Weights"
network:
  dynamics:
    hhcell:
      name: hhcell
      iri: neuroml:cell
      parameters:
        v0: { value: -65, unit: mV }
        specificCapacitance: { value: 1.0, unit: uF_per_cm2 }
        spikeThresh: { value: -20, unit: mV }
        diameter: { value: 17.841242 }
      components:
        passiveChan:
          name: passiveChan
          iri: neuroml:ionChannelHH
          parameters:
            conductance: { value: 10, unit: pS }
            condDensity: { value: 0.0003, unit: S_per_cm2 }
            erev: { value: -54.3, unit: mV }
            ion: non_specific
        naChan:
          name: naChan
          iri: neuroml:ionChannelHH
          parameters:
            conductance: { value: 10, unit: pS }
            condDensity: { value: 120.0, unit: mS_per_cm2 }
            erev: { value: 50.0, unit: mV }
            ion: na
            species: na
          components:
            m:
              name: m
              iri: neuroml:gateHHrates
              parameters: { instances: { value: 3 } }
              components:
                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 } }
              components:
                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 }
            condDensity: { value: 36, unit: mS_per_cm2 }
            erev: { value: -77, unit: mV }
            ion: k
            species: k
          components:
            n:
              name: n
              iri: neuroml:gateHHrates
              parameters: { instances: { value: 4 } }
              components:
                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 }
    pulseGen2:
      name: pulseGen2
      iri: neuroml:pulseGenerator
  nodes:
    # Pre-synaptic cells (3)
    - { id: 0, dynamics: hhcell }
    - { id: 1, dynamics: hhcell }
    - { id: 2, dynamics: hhcell }
    # Post-synaptic cells (9): chemical (0-2), electrical (3-5), continuous (6-8)
    - { id: 10, dynamics: hhcell }
    - { id: 11, dynamics: hhcell }
    - { id: 12, dynamics: hhcell }
    - { id: 13, dynamics: hhcell }
    - { id: 14, dynamics: hhcell }
    - { id: 15, dynamics: hhcell }
    - { id: 16, dynamics: hhcell }
    - { id: 17, dynamics: hhcell }
    - { id: 18, dynamics: hhcell }
    # Input nodes (one pulseGenerator, connected to 3 pre-cells with weights)
    - id: 100
      dynamics: pulseGen2
      parameters:
        delay: { value: 100, unit: ms }
        duration: { value: 100, unit: ms }
        amplitude: { value: 0.068, unit: nA }
  edges:
    # Weighted current inputs to pre-synaptic cells
    - { source: 100, target: 0, parameters: { weight: { value: 1 } } }
    - { source: 100, target: 1, parameters: { weight: { value: 0.5 } } }
    - { source: 100, target: 2, parameters: { weight: { value: 2 } } }
    # Chemical projections: pre[0] → post[10,11,12]
    - source: 0
      target: 10
      coupling: expOneSynapse
      parameters:
        gbase: { value: 0.2, unit: nS }
        erev: { value: 0, unit: mV }
        tauDecay: { value: 2, unit: ms }
    - source: 0
      target: 11
      coupling: expOneSynapse
      parameters:
        gbase: { value: 0.2, unit: nS }
        erev: { value: 0, unit: mV }
        tauDecay: { value: 2, unit: ms }
        weight: { value: 0.5 }
        delay: { value: 20, unit: ms }
    - source: 0
      target: 12
      coupling: expOneSynapse
      parameters:
        gbase: { value: 0.2, unit: nS }
        erev: { value: 0, unit: mV }
        tauDecay: { value: 2, unit: ms }
        weight: { value: 2 }
        delay: { value: 40, unit: ms }
    # Electrical projections (gap junctions): pre[0] → post[13,14,15]
    - source: 0
      target: 13
      coupling: gapJunction
      parameters:
        conductance: { value: 100, unit: pS }
        weight: { value: 1 }
    - source: 0
      target: 14
      coupling: gapJunction
      parameters:
        conductance: { value: 100, unit: pS }
        weight: { value: 0.5 }
    - source: 0
      target: 15
      coupling: gapJunction
      parameters:
        conductance: { value: 100, unit: pS }
        weight: { value: 2 }
    # Continuous projections (graded synapses): pre[0] → post[16,17,18]
    - source: 0
      target: 16
      coupling: gradedSynapse
      parameters:
        conductance: { value: 0.1, unit: nS }
        delta: { value: 5, unit: mV }
        Vth: { value: -35, unit: mV }
        k: { value: 0.025, unit: per_ms }
        erev: { value: 0, unit: mV }
        weight: { value: 1 }
    - source: 0
      target: 17
      coupling: gradedSynapse
      parameters:
        conductance: { value: 0.1, unit: nS }
        delta: { value: 5, unit: mV }
        Vth: { value: -35, unit: mV }
        k: { value: 0.025, unit: per_ms }
        erev: { value: 0, unit: mV }
        weight: { value: 0.5 }
    - source: 0
      target: 18
      coupling: gradedSynapse
      parameters:
        conductance: { value: 0.1, unit: nS }
        delta: { value: 5, unit: mV }
        Vth: { value: -35, unit: mV }
        k: { value: 0.025, unit: per_ms }
        erev: { value: 0, unit: mV }
        weight: { value: 2 }

integration:
  method: euler
  step_size: 0.005
  duration: 300.0
  time_scale: ms
""")
print(f"Network nodes: {len(exp.network.nodes)}")
print(f"Network edges: {len(exp.network.edges)}")

Network nodes: 13
Network edges: 12

2. Render LEMS XML

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

<Lems>
  <Target component="sim_NeuroML_Ex26__Weights"/>

  <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="hhcell">
        <morphology id="hhcell_morphology">
            <segment id="0" name="Soma">
                <proximal x="0.0" y="0.0" z="0.0" diameter="17.841242"/>
                <distal x="0.0" y="0.0" z="0.0" diameter="17.841242"/>
            </segment>
            <segmentGroup id="all">
                <member segment="0"/>
            </segmentGroup>
            <segmentGroup id="soma_group">
                <member segment="0"/>
            </segmentGroup>
        </morphology>
        <biophysicalProperties id="biophys">
            <membraneProperties>
                <channelDensity condDensity="36 mS_per_cm2" id="kChan_all" ionChannel="kChan" erev="-77 mV" ion="k"/>
                <channelDensity condDensity="120 mS_per_cm2" id="naChan_all" ionChannel="naChan" erev="5

3. Run Reference

from tvbo.adapters.neuroml import run_lems_example

ref_outputs = run_lems_example("LEMS_NML2_Ex26_Weights.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  ex26.dat: shape=(60001, 10)
  ex26_pre.dat: shape=(60001, 4)

4. Run TVBO

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

TVBO: ('time', 'quantity'), shape=(60001, 12)

5. Compare & Plot

from tvbo.adapters.neuroml import plot_lems_comparison
plot_lems_comparison("LEMS_NML2_Ex26_Weights.xml", ref_outputs, result.integration.data, title_prefix="Ex26")