Ex23: Spike Times

Recording spike times from a mixed IaF / PyNN / HH network with Poisson inputs

Model: Spike Time Recording

Demonstrates <EventOutputFile> — recording discrete spike events from a three-population network driven by poissonFiringSynapse sources.

---

1. Define Network in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex23: Spike Times"
dynamics:
  name: iaf
  iri: neuroml:iafCell
network:
  dynamics:
    # ── Cell types ──
    iaf:
      name: iaf
      iri: neuroml:iafCell
      parameters:
        leakConductance: {value: 0.2, unit: nS}
        leakReversal:    {value: -65, unit: mV}
        thresh:          {value: -55, unit: mV}
        reset:           {value: -70, unit: mV}
        C:               {value: 3.2, unit: pF}

    IF_curr_alpha:
      name: IF_curr_alpha
      iri: neuroml:IF_curr_alpha
      parameters:
        cm:         {value: 1.0}
        i_offset:   {value: 0}
        tau_m:      {value: 20.0}
        tau_refrac: {value: 2}
        tau_syn_E:  {value: 0.5}
        tau_syn_I:  {value: 0.5}
        v_init:     {value: -65}
        v_reset:    {value: -62.0}
        v_rest:     {value: -65.0}
        v_thresh:   {value: -52.0}

    hhcell:
      name: hhcell
      iri: neuroml:cell
      parameters:
        diameter:            {value: 17.841242}
        specificCapacitance: {value: 1.0, unit: uF_per_cm2}
        initMembPotential:   {value: -65, unit: mV}
        spikeThresh:         {value: -20, unit: mV}
        resistivity:         {value: 0.03, unit: kohm_cm}
      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:         {description: non_specific}
        naChan:
          name: naChan
          iri: neuroml:ionChannelHH
          parameters:
            conductance: {value: 10, unit: pS}
            condDensity: {value: 0.12, unit: S_per_cm2}
            erev:        {value: 50, unit: mV}
            ion:         {description: na}
            species:     {description: 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: 0.036, unit: S_per_cm2}
            erev:        {value: -77, unit: mV}
            ion:         {description: k}
            species:     {description: 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}

    # ── Synapses ──
    syn_exc1:
      name: syn_exc1
      iri: neuroml:expTwoSynapse
      parameters:
        gbase:    {value: 0.1, unit: nS}
        erev:     {value: 0, unit: mV}
        tauDecay: {value: 10, unit: ms}
        tauRise:  {value: 0.5, unit: ms}

    syn_exc2:
      name: syn_exc2
      iri: neuroml:expTwoSynapse
      parameters:
        gbase:    {value: 0.3, unit: nS}
        erev:     {value: 0, unit: mV}
        tauDecay: {value: 10, unit: ms}
        tauRise:  {value: 0.5, unit: ms}

    syn_exc3:
      name: syn_exc3
      iri: neuroml:expTwoSynapse
      parameters:
        gbase:    {value: 5, unit: nS}
        erev:     {value: 0, unit: mV}
        tauDecay: {value: 10, unit: ms}
        tauRise:  {value: 0.5, unit: ms}

    # ── Poisson spike sources ──
    poissonFiringSyn:
      name: poissonFiringSyn
      iri: neuroml:poissonFiringSynapse
      parameters:
        averageRate: {value: 50, unit: Hz}
        synapse:     {description: syn_exc1}
        spikeTarget: {description: "./syn_exc1"}

    poissonFiringSyn2:
      name: poissonFiringSyn2
      iri: neuroml:poissonFiringSynapse
      parameters:
        averageRate: {value: 150, unit: Hz}
        synapse:     {description: syn_exc3}
        spikeTarget: {description: "./syn_exc3"}

  # ── Populations ──
  nodes:
    # iafPop (5 cells)
    - {id: 0, dynamics: iaf}
    - {id: 1, dynamics: iaf}
    - {id: 2, dynamics: iaf}
    - {id: 3, dynamics: iaf}
    - {id: 4, dynamics: iaf}
    # pynnPop (3 cells) — not in OutputFile, only in EventOutputFile
    - {id: 10, dynamics: IF_curr_alpha, record: false}
    - {id: 11, dynamics: IF_curr_alpha, record: false}
    - {id: 12, dynamics: IF_curr_alpha, record: false}
    # hhpop (1 cell)
    - {id: 20, dynamics: hhcell}
    # spike sources (explicitInput, not populations)
    - {id: 100, dynamics: poissonFiringSyn, record: false}
    - {id: 101, dynamics: poissonFiringSyn2, record: false}

  # ── Connectivity ──
  edges:
    # Poisson → iafPop
    - {source: 100, target: 0}
    - {source: 100, target: 1}
    - {source: 100, target: 2}
    - {source: 100, target: 3}
    - {source: 100, target: 4}
    # Poisson → pynnPop
    - {source: 101, target: 10}
    - {source: 101, target: 11}
    - {source: 101, target: 12}
    # iafPop → hhpop via syn_exc2
    - {source: 0, target: 20, coupling: syn_exc2}
    - {source: 1, target: 20, coupling: syn_exc2}
    - {source: 2, target: 20, coupling: syn_exc2}
    - {source: 3, target: 20, coupling: syn_exc2}
    - {source: 4, target: 20, coupling: syn_exc2}

integration:
  method: euler
  step_size: 0.05
  duration: 300.0
  time_scale: ms
  parameters:
    seed: {value: 123}
""")
print(f"Network dynamics: {list(exp.network.dynamics.keys())}")

Network dynamics: ['IF_curr_alpha', 'hhcell', 'iaf', 'poissonFiringSyn', 'poissonFiringSyn2', 'syn_exc1', 'syn_exc2', 'syn_exc3']

2. Render LEMS XML

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

<Lems>
  <Target component="sim_NeuroML_Ex23__Spike_Times"/>

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

    <iafCell id="iaf" C="3.2 pF" leakConductance="0.2 nS" leakReversal="-65 mV" reset="-70 mV" thresh="-55 mV"/>

    <IF_curr_alpha id="IF_curr_alpha" cm="1" i_offset="0" tau_m="20" tau_refrac="2" tau_syn_E="0.5" tau_syn_I="0.5" v_init="-65" v_reset="-62" v_rest="-65" v_thresh="-52"/>

    <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>
 

3. Run Reference

from tvbo.adapters.neuroml import run_lems_example

ref_outputs = run_lems_example("LEMS_NML2_Ex23_Spiketimes.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  ex23.hh.dat: shape=(6001, 2)
  ex23.iaf.dat: shape=(6001, 6)

4. Run TVBO

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

TVBO: ('time', 'quantity'), shape=(6001, 6)

5. Compare & Plot

from tvbo.adapters.neuroml import plot_lems_comparison
plot_lems_comparison("LEMS_NML2_Ex23_Spiketimes.xml", ref_outputs, result.integration.data, title_prefix="Ex23")