Ex12: Network 2

Multiple spike sources and synapse types including NMDA with Mg²⁺ block

Interoperability·NeuroML / LEMS

Model: Network 2

Nine iafCells receiving input from three spike sources (periodic generator, spike arrays) via three synapse types: expOneSynapse, expTwoSynapse, and NMDA blockingPlasticSynapse with Mg²⁺ voltage-dependent block. Demonstrates weight and delay modulation.

Reference: NeuroML2 LEMS_NML2_Ex12_Net2.xml

---

1. Define Network in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex12: Network 2"
dynamics:
  name: iafCell
  iri: neuroml:iafCell
network:
  dynamics:
    iaf1:
      name: iaf1
      iri: neuroml:iafCell
      parameters:
        leakReversal: {value: -60, unit: mV}
        thresh: {value: -35, unit: mV}
        reset: {value: -65, unit: mV}
        C: {value: 10, unit: pF}
        leakConductance: {value: 0.5, unit: nS}
    spiker:
      name: spiker
      iri: neuroml:spikeGenerator
      parameters:
        period: {value: 30, unit: ms}
    spikes2:
      name: spikes2
      iri: neuroml:spikeArray
      events:
        s:
          event_type: preset_time
          trigger_times: [50, 100, 130]
    spike100:
      name: spike100
      iri: neuroml:spikeArray
      events:
        s:
          event_type: preset_time
          trigger_times: [100]
    # NMDA synapse with Mg²⁺ voltage-dependent block (defined with full
    # composition tree — blockMechanism is a child dynamics)
    synNmda:
      name: synNmda
      iri: neuroml:blockingPlasticSynapse
      parameters:
        gbase: {value: 5, unit: nS}
        tauDecay: {value: 10, unit: ms}
        tauRise: {value: 1, unit: ms}
        erev: {value: 0, unit: mV}
      modes:
        blockMechanism:
          name: blockMechanism
          iri: neuroml:voltageConcDepBlockMechanism
          parameters:
            species: {description: mg}
            blockConcentration: {value: 1.2, unit: mM}
            scalingConc: {value: 1.9205441817997078, unit: mM}
            scalingVolt: {value: 16.129032258064516, unit: mV}
  nodes:
    # 9 post-synaptic cells
    - {id: 0, dynamics: iaf1}
    - {id: 1, dynamics: iaf1}
    - {id: 2, dynamics: iaf1}
    - {id: 3, dynamics: iaf1}
    - {id: 4, dynamics: iaf1}
    - {id: 5, dynamics: iaf1}
    - {id: 6, dynamics: iaf1}
    - {id: 7, dynamics: iaf1}
    - {id: 8, dynamics: iaf1}
    # Spike sources
    - {id: 100, dynamics: spike100}
    - {id: 101, dynamics: spikes2}
    - {id: 102, dynamics: spiker}
  edges:
    # spike100 → iaf[0..2]: expOneSynapse, varying weight/delay
    - {source: 100, target: 0, coupling: expOneSynapse, parameters: {gbase: {value: 0.1, unit: nS}, erev: {value: 0, unit: mV}, tauDecay: {value: 2, unit: ms}}}
    - {source: 100, target: 1, coupling: expOneSynapse, parameters: {gbase: {value: 0.1, unit: nS}, erev: {value: 0, unit: mV}, tauDecay: {value: 2, unit: ms}, weight: {value: 0.5}, delay: {value: 10, unit: ms}}}
    - {source: 100, target: 2, coupling: expOneSynapse, parameters: {gbase: {value: 0.1, unit: nS}, erev: {value: 0, unit: mV}, tauDecay: {value: 2, unit: ms}, weight: {value: 0.25}, delay: {value: 15, unit: ms}}}
    # spikes2 → iaf[3..5]: expTwoSynapse, varying weight/delay
    - {source: 101, target: 3, coupling: expTwoSynapse, parameters: {gbase: {value: 0.1, unit: nS}, erev: {value: 0, unit: mV}, tauDecay: {value: 2, unit: ms}, tauRise: {value: 0.05, unit: ms}}}
    - {source: 101, target: 4, coupling: expTwoSynapse, parameters: {gbase: {value: 0.1, unit: nS}, erev: {value: 0, unit: mV}, tauDecay: {value: 2, unit: ms}, tauRise: {value: 0.05, unit: ms}, weight: {value: 0.5}, delay: {value: 10, unit: ms}}}
    - {source: 101, target: 5, coupling: expTwoSynapse, parameters: {gbase: {value: 0.1, unit: nS}, erev: {value: 0, unit: mV}, tauDecay: {value: 2, unit: ms}, tauRise: {value: 0.05, unit: ms}, weight: {value: 0.25}, delay: {value: 15, unit: ms}}}
    # spiker → iaf[6..8]: NMDA synapse, varying weight/delay
    - {source: 102, target: 6, coupling: synNmda}
    - {source: 102, target: 7, coupling: synNmda, parameters: {weight: {value: 0.5}, delay: {value: 10, unit: ms}}}
    - {source: 102, target: 8, coupling: synNmda, parameters: {weight: {value: 0.25}, delay: {value: 15, unit: ms}}}
integration:
  method: euler
  step_size: 0.005
  duration: 300.0
  time_scale: ms
""")
print(f"Model: {exp.dynamics.name if exp.dynamics else 'network'}")

Model: iafCell

2. Render LEMS XML

xml = exp.render("lems")
print(xml[:3000])

<Lems>

  <!-- Tell jLEMS/jNeuroML which component is the simulation entry point. -->
  <Target component="sim_NeuroML_Ex12__Network_2"/>

  <!-- ════════════════════════════════════════════════════════════════
       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: iaf1 ── -->
  <ComponentType name="iaf1" extends="baseCellMembPot">
    <Parameter name="tau" dimension="none"/>
    <Parameter name="leakReversal" dimension="voltage"/>
    <Parameter name="thresh" dimension="voltage"/>
    <Parameter name="reset" dimension="voltage"/>
    <Parameter name="C" dimension="capacitance"/>
    <Parameter name="leakConductance" dimension="conductance"/>
    <Parameter name="refract" dimension="time"/>
    <Parameter name="v_0" dimension="none" />
    <!-- Dynamically attached synapses/inputs from network connections -->
    <Attachments name="synapses" type="basePointCurrent"/>

    <Dynamics>
      <StateVariable name="v" dimension="none" exposure="v"/>
      <StateVariable name="lastSpikeTime" dimension="time"/>
      <ConditionalDerivedVariable name="I_ext" dimension="none">
        <Case condition="t .geq. 0.1 .and. t .lt. 0.2" value="0.300000000000000"/>
        <Case value="0.0"/>
      </ConditionalDerivedVariable>
      <!-- ── Regime-based dynamics (spike model) ── -->
      <OnStart>
        <StateAssignment variable="v" value="v_0"/>
      </OnStart>

      <Regime name="integrating" initial="true">
        <TimeDerivative variable="v" value="I_ext + (leakReversal - v)/tau"/>
        <OnCondition test="v .gt. thresh">
          <EventOut port="spike"/>
          <Transition regime="refractory"/>
        </OnCondition>
      </Regime>

      <Regime name="refractory">
        <OnEntry>
          <StateAssignment variable="lastSpikeTime" value="t"/>
          <StateAssignment variable="v" value="reset"/>
        </OnEntry>
        <OnCondition test="t .gt. lastSpikeTime + refract">
          <Transition regime="integrating"/>
        </OnCondition>
      </Regime>
    </Dynamics>
  </ComponentType>

  <Component id="iaf1_inst" type="iaf1" tau="20.0" leakReversal="-60 mV" thresh="-35 mV" reset="-65 mV" C="10 pF" leakConductance="0.5 nS" refract="0 ms" v_0="-60.0"/>


  <!-- ════════════════════════════════════════════════════════════════
       Built-in/named synapse definitions (no custom ODE dynamics)
       ════════════════════════════════════════════════════════════════ -->
  <!-- Synapse: expOneSynapse (expOneSynapse) -->

3. Run Reference

from tvbo.adapters.neuroml import run_lems_example

ref_outputs = run_lems_example("LEMS_NML2_Ex12_Net2.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  ex12.dat: shape=(60001, 10)

4. Run TVBO

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

pyNeuroML >>> 12:58:49 - INFO - Loading LEMS file: tvbo_lems_sim.xml and running with jNeuroML
pyNeuroML >>> 12:58:49 - 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  -nogui -I '') in directory: /var/folders/ym/9kw1g21j1nd7kwfn8c0z3st40000gn/T/tmpieccy124
pyNeuroML >>> 12:58:50 - INFO - Command completed successfully!

TVBO: ('time', 'quantity'), shape=(60001, 9)

5. Compare & Plot

from tvbo.adapters.neuroml import plot_lems_comparison
plot_lems_comparison("LEMS_NML2_Ex12_Net2.xml", ref_outputs, result.integration.data, title_prefix="Ex12")