Ex23: Spike Times

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

Interoperability·NeuroML / LEMS

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']

* Owlready2 * Warning: ignoring cyclic subclass of/subproperty of, involving:
  http://uri.interlex.org/tgbugs/uris/readable/atlas/Space

2. Render LEMS XML

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

<Lems>

  <!-- Tell jLEMS/jNeuroML which component is the simulation entry point. -->
  <Target component="sim_NeuroML_Ex23__Spike_Times"/>

  <!-- ════════════════════════════════════════════════════════════════
       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: iaf ── -->
  <ComponentType name="iaf" 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="

/Users/leonmartin_bih/tools/tvbo/tvbo/adapters/neuroml.py:3082: UserWarning: NeuroML type 'poissonFiringSynapse' (component 'poissonFiringSyn') requires a 'synapse' reference to a 'baseSynapse' component, but none was given. Declare it as a parameter, e.g. synapse: {value: <DynamicsName>}.
  net_ctx = _build_network_context(experiment)
/Users/leonmartin_bih/tools/tvbo/tvbo/adapters/neuroml.py:3082: UserWarning: NeuroML type 'poissonFiringSynapse' (component 'poissonFiringSyn2') requires a 'synapse' reference to a 'baseSynapse' component, but none was given. Declare it as a parameter, e.g. synapse: {value: <DynamicsName>}.
  net_ctx = _build_network_context(experiment)

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}")

pyNeuroML >>> 13:06:10 - INFO - Loading LEMS file: tvbo_lems_sim.xml and running with jNeuroML
pyNeuroML >>> 13:06:10 - 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/tmp5ehpf4rs
pyNeuroML >>> 13:06:11 - INFO - Command completed successfully!

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")