Ex16: Input Types

Various input types — Izhikevich 2007 RS cells with 13 different NeuroML input generators

Model: Input Types

Demonstrates all NeuroML2 input generator types applied to Izhikevich 2007 RS cells. 13 cells each receive a different input: pulse, sine, ramp, voltage clamp, spike array, spike generators (regular, random, Poisson, refractory Poisson), Poisson firing synapse, compound input, transient Poisson firing synapse, and timed synaptic input.

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1. Define Full Network in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex16: Input Types"
dynamics:
  name: RS
  iri: neuroml:izhikevich2007Cell
  parameters:
    v0:    { value: -60, unit: mV }
    C:     { value: 100, unit: pF }
    k:     { value: 0.7, unit: nS_per_mV }
    vr:    { value: -60, unit: mV }
    vt:    { value: -40, unit: mV }
    vpeak: { value: 35, unit: mV }
    a:     { value: 0.03, unit: per_ms }
    b:     { value: -2, unit: nS }
    c:     { value: -50, unit: mV }
    d:     { value: 100, unit: pA }
network:
  dynamics:
    # ── Synapse used by spike sources ──
    syn1:
      name: syn1
      iri: neuroml:expTwoSynapse
      parameters:
        gbase: { value: 1, unit: nS }
        erev:  { value: 20, unit: mV }
        tauRise: { value: 0.1, unit: ms }
        tauDecay: { value: 3, unit: ms }
    # ── Synapses used by poissonFiringSynapse / transientPoisson / timedSynapticInput ──
    synInput:
      name: synInput
      iri: neuroml:expTwoSynapse
      parameters:
        gbase: { value: 8, unit: nS }
        erev:  { value: 20, unit: mV }
        tauRise: { value: 1, unit: ms }
        tauDecay: { value: 5, unit: ms }
    synInputFast:
      name: synInputFast
      iri: neuroml:expTwoSynapse
      parameters:
        gbase: { value: 1, unit: nS }
        erev:  { value: 20, unit: mV }
        tauRise: { value: 0.2, unit: ms }
        tauDecay: { value: 1, unit: ms }
    synInputFastTwo:
      name: synInputFastTwo
      iri: neuroml:expThreeSynapse
      parameters:
        gbase1: { value: 1.5, unit: nS }
        tauRise: { value: 0.1, unit: ms }
        tauDecay1: { value: 0.7, unit: ms }
        gbase2: { value: 0.5, unit: nS }
        tauDecay2: { value: 2.5, unit: ms }
        erev: { value: 0, unit: mV }
    # ── Current input generators ──
    pulseGen0:
      name: pulseGen0
      iri: neuroml:pulseGenerator
      parameters:
        delay: { value: 50, unit: ms }
        duration: { value: 200, unit: ms }
        amplitude: { value: 1, unit: nA }
    sg0:
      name: sg0
      iri: neuroml:sineGenerator
      parameters:
        phase: { value: 0 }
        delay: { value: 50, unit: ms }
        duration: { value: 200, unit: ms }
        amplitude: { value: 1.4, unit: nA }
        period: { value: 50, unit: ms }
    rg0:
      name: rg0
      iri: neuroml:rampGenerator
      parameters:
        delay: { value: 50, unit: ms }
        duration: { value: 200, unit: ms }
        startAmplitude: { value: 0.5, unit: nA }
        finishAmplitude: { value: 4, unit: nA }
        baselineAmplitude: { value: 0, unit: nA }
    vClamp0:
      name: vClamp0
      iri: neuroml:voltageClampTriple
      parameters:
        active: { value: 1 }
        delay: { value: 50, unit: ms }
        duration: { value: 200, unit: ms }
        conditioningVoltage: { value: -70, unit: mV }
        testingVoltage: { value: -50, unit: mV }
        returnVoltage: { value: -70, unit: mV }
        simpleSeriesResistance: { value: 1.0e6, unit: ohm }
    # ── Event sources (become populations + synapticConnection) ──
    spkArr:
      name: spkArr
      iri: neuroml:spikeArray
      events:
        spikes:
          event_type: preset_time
          trigger_times: [50, 100, 150, 155, 250]
    spikeGenRegular:
      name: spikeGenRegular
      iri: neuroml:spikeGenerator
      parameters:
        period: { value: 20, unit: ms }
    spikeGenRandom:
      name: spikeGenRandom
      iri: neuroml:spikeGeneratorRandom
      parameters:
        minISI: { value: 10, unit: ms }
        maxISI: { value: 30, unit: ms }
    spikeGenPoisson:
      name: spikeGenPoisson
      iri: neuroml:spikeGeneratorPoisson
      parameters:
        averageRate: { value: 50, unit: Hz }
    spikeGenRefPoisson:
      name: spikeGenRefPoisson
      iri: neuroml:spikeGeneratorRefPoisson
      parameters:
        averageRate: { value: 50, unit: Hz }
        minimumISI: { value: 10, unit: ms }
    # ── Firing synapse inputs (current inputs with built-in synapse) ──
    poissonFiringSyn:
      name: poissonFiringSyn
      iri: neuroml:poissonFiringSynapse
      parameters:
        averageRate: { value: 10, unit: Hz }
        synapse: { description: synInput }
        spikeTarget: { description: "./synInput" }
    ci0:
      name: ci0
      iri: neuroml:compoundInput
      components:
        pg1:
          name: pg1
          iri: neuroml:pulseGenerator
          parameters:
            delay: { value: 50, unit: ms }
            duration: { value: 200, unit: ms }
            amplitude: { value: 0.8, unit: nA }
        pg2:
          name: pg2
          iri: neuroml:pulseGenerator
          parameters:
            delay: { value: 100, unit: ms }
            duration: { value: 100, unit: ms }
            amplitude: { value: 0.4, unit: nA }
        sg0:
          name: sg0
          iri: neuroml:sineGenerator
          parameters:
            phase: { value: 0 }
            delay: { value: 125, unit: ms }
            duration: { value: 50, unit: ms }
            amplitude: { value: 0.4, unit: nA }
            period: { value: 25, unit: ms }
    transPoissonFiringSyn:
      name: transPoissonFiringSyn
      iri: neuroml:transientPoissonFiringSynapse
      parameters:
        delay: { value: 50, unit: ms }
        duration: { value: 50, unit: ms }
        averageRate: { value: 300, unit: Hz }
        synapse: { description: synInputFast }
        spikeTarget: { description: "./synInputFast" }
    synTrain:
      name: synTrain
      iri: neuroml:timedSynapticInput
      parameters:
        synapse: { description: synInputFastTwo }
        spikeTarget: { description: "./synInputFastTwo" }
      events:
        spikes:
          event_type: preset_time
          trigger_times: [2, 15, 27, 40, 45, 50, 52, 54, 54.5, 54.6, 54.7, 54.8, 54.9, 55, 55.1, 55.2]
  nodes:
    - {id: 0, dynamics: RS}
    - {id: 1, dynamics: RS}
    - {id: 2, dynamics: RS}
    - {id: 3, dynamics: RS}
    - {id: 4, dynamics: RS}
    - {id: 5, dynamics: RS}
    - {id: 6, dynamics: RS}
    - {id: 7, dynamics: RS}
    - {id: 8, dynamics: RS}
    - {id: 9, dynamics: RS}
    - {id: 10, dynamics: RS, record: false}
    - {id: 11, dynamics: RS, record: false}
    - {id: 12, dynamics: RS, record: false}
    # Input source nodes
    - {id: 100, dynamics: pulseGen0, record: false}
    - {id: 101, dynamics: sg0, record: false}
    - {id: 102, dynamics: rg0, record: false}
    - {id: 103, dynamics: vClamp0, record: false}
    - {id: 104, dynamics: spkArr, record: false}
    - {id: 105, dynamics: spikeGenRegular, record: false}
    - {id: 107, dynamics: spikeGenPoisson, record: false}
    - {id: 112, dynamics: spikeGenRefPoisson, record: false}
    - {id: 106, dynamics: spikeGenRandom, record: false}
    - {id: 108, dynamics: poissonFiringSyn, record: false}
    - {id: 109, dynamics: ci0, record: false}
    - {id: 110, dynamics: transPoissonFiringSyn, record: false}
    - {id: 111, dynamics: synTrain, record: false}
  edges:
    # Current inputs → explicitInput
    - {source: 100, target: 0}
    - {source: 101, target: 1}
    - {source: 102, target: 2}
    - {source: 103, target: 3}
    - {source: 108, target: 8}
    - {source: 109, target: 9}
    - {source: 110, target: 10}
    - {source: 111, target: 11}
    # Spike sources → synapticConnection via syn1
    - {source: 104, target: 4, coupling: syn1}
    - {source: 105, target: 5, coupling: syn1}
    - { source: 106, target: 6, coupling: syn1 }
    - { source: 107, target: 7, coupling: syn1 }
    - { source: 112, target: 12, coupling: syn1 }
integration:
  method: euler
  step_size: 0.001
  duration: 300.0
  time_scale: ms
  parameters:
    seed: { value: 123 }
""")
print(f"Model: {exp.dynamics.name if exp.dynamics else 'network'}")
print(f"Network nodes: {len(exp.network.nodes)}")

Model: RS
Network nodes: 26

2. Render LEMS XML

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

<Lems>
  <Target component="sim_NeuroML_Ex16__Input_Types"/>

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

    <izhikevich2007Cell id="RS" C="100 pF" a="0.03 per_ms" b="-2 nS" c="-50 mV" d="100 pA" k="0.7 nS_per_mV" v0="-60 mV" vpeak="35 mV" vr="-60 mV" vt="-40 mV"/>

    <pulseGenerator id="pulseGen0" amplitude="1.0 nA" delay="50.0 ms" duration="200.0 ms"/>

    <sineGenerator id="sg0" amplitude="1.4 nA" delay="50.0 ms" duration="200.0 ms" period="50.0 ms" phase="0.0"/>

    <rampGenerator id="rg0" baselineAmplitude="0.0 nA" delay="50.0 ms" duration="200.0 ms" finishAmplitude="4.0 nA" startAmplitude="0.5 nA"/>

    <voltageClampTriple id="vClamp0" active="1.0" conditioningVoltage="-70.0 mV" delay="50.0 ms" duration="200.0 ms" returnVoltage="-70.0 mV" simpleSeriesResistance="1000000.0 ohm" testingVoltage="-50.0 mV"/>

    <spikeArray id="spkArr">
        <spike id="0" time="50.0 ms"/>
        <spike id="1" time="100.0 ms"/>
        <spike id="2" time="150.0 ms"/>
        <spike id="3" time="155.0 ms"/>
        <spike id="4" time="250.0 ms"/>
    </spikeArray>

    <spikeGenerator id="spikeGenRegular" period="20.0 ms"/>

    <spikeGeneratorPoisson id="spikeGenPoisson" averageRate="50.0 Hz"/>

    <spikeGeneratorRefPoisson id="spikeGenRefPoisson" averageRate="50.0 Hz" minimumISI="10.0 ms"/>

    <spikeGeneratorRandom id="spikeGenRandom" maxISI="30.0 ms" minISI="10.0 ms"/>

    <poissonFiringSynapse id="poissonFiringSyn" averageRate="10.0 Hz" spikeTarget="./synInput" synapse="synInput"/>

    <compoundInput id="ci0">
        <pulseGenerator id="pg1" amplitude="0.8 nA" delay="50 ms" duration="200 ms"/>
        <pulseGenerator id="pg2" amplitude="0.4 nA" delay="100 ms" duration="100 ms"/>
        <sineGenerator id="sg0" amplitude="0.4 nA" delay="125 ms" duration="50 ms" period="25 ms" phase="0"/>
    </compoundInput>

    <transientPoissonFiringSynapse id="transPoissonFiringSyn" averageR

3. Run Reference

from tvbo.adapters.neuroml import run_lems_example

ref_outputs = run_lems_example("LEMS_NML2_Ex16_Inputs.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  ex16_v.dat: shape=(300001, 11)

4. Run TVBO

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

TVBO: ('time', 'quantity'), shape=(300001, 10)

5. Compare & Plot

from tvbo.adapters.neuroml import plot_lems_comparison
plot_lems_comparison("LEMS_NML2_Ex16_Inputs.xml", ref_outputs, result.integration.data, title_prefix="Ex16")