Ex3: HH Network with Synapses

Network of HH cells connected by expOneSynapse, expTwoSynapse, and alphaSynapse

Model: HH Network

This example connects a pre-synaptic HH cell to three post-synaptic passive cells via different synapse types (expOneSynapse, expTwoSynapse, alphaSynapse).

Cell types use standard NeuroML types (pointCellCondBased) with hierarchical components: composition trees. Synapses use standard NeuroML synapse types on network edges. This gives exact numerical identity with the reference jNeuroML simulation.

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1. Define Network in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex3: HH Network"
dynamics:
  name: hhcell_1
  iri: neuroml:pointCellCondBased
  parameters:
    C:      { value: 10, unit: pF }
    v0:     { value: -65, unit: mV }
    thresh: { value: 20, unit: mV }
  components:
    passive:
      name: passive
      iri: neuroml:ionChannelPassive
      parameters:
        conductance: { value: 10, unit: pS }
        number:      { value: 300 }
        erev:        { value: -54.3, unit: mV }
    na:
      name: na
      iri: neuroml:ionChannelHH
      parameters:
        conductance: { value: 10, unit: pS }
        number:      { value: 120000 }
        erev:        { value: 50, unit: mV }
      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 }
    k:
      name: k
      iri: neuroml:ionChannelHH
      parameters:
        conductance: { value: 10, unit: pS }
        number:      { value: 36000 }
        erev:        { value: -77, unit: mV }
      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 }

network:
  number_of_nodes: 5
  dynamics:
    hhcell_1:
      name: hhcell_1
      iri: neuroml:pointCellCondBased
      parameters:
        C:      { value: 10, unit: pF }
        v0:     { value: -65, unit: mV }
        thresh: { value: 20, unit: mV }
      components:
        passive:
          name: passive
          iri: neuroml:ionChannelPassive
          parameters:
            conductance: { value: 10, unit: pS }
            number:      { value: 300 }
            erev:        { value: -54.3, unit: mV }
        na:
          name: na
          iri: neuroml:ionChannelHH
          parameters:
            conductance: { value: 10, unit: pS }
            number:      { value: 120000 }
            erev:        { value: 50, unit: mV }
          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 }
        k:
          name: k
          iri: neuroml:ionChannelHH
          parameters:
            conductance: { value: 10, unit: pS }
            number:      { value: 36000 }
            erev:        { value: -77, unit: mV }
          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 }
    hhcell_2:
      name: hhcell_2
      iri: neuroml:pointCellCondBased
      parameters:
        C:      { value: 10, unit: pF }
        v0:     { value: -55, unit: mV }
        thresh: { value: 20, unit: mV }
      components:
        leak:
          name: leak
          iri: neuroml:ionChannelPassive
          parameters:
            conductance: { value: 10, unit: pS }
            number:      { value: 300 }
            erev:        { value: -54.3, unit: mV }
  nodes:
    - id: 0
      dynamics: hhcell_1
      record: false
    - id: 1
      dynamics: hhcell_2
    - id: 2
      dynamics: hhcell_2
    - id: 3
      dynamics: hhcell_2
    - id: 100
      dynamics: pulseGenerator
      parameters:
        delay:     { value: 25, unit: ms }
        duration:  { value: 50, unit: ms }
        amplitude: { value: 0.065, unit: nA }
  edges:
    - source: 100
      target: 0
    - source: 0
      target: 1
      coupling: expOneSynapse
      parameters:
        gbase:    { value: 0.5, unit: nS }
        erev:     { value: 0, unit: mV }
        tauDecay: { value: 3, unit: ms }
    - source: 0
      target: 2
      coupling: expTwoSynapse
      parameters:
        gbase:    { value: 0.5, unit: nS }
        erev:     { value: 0, unit: mV }
        tauRise:  { value: 1, unit: ms }
        tauDecay: { value: 2, unit: ms }
    - source: 0
      target: 3
      coupling: alphaSynapse
      parameters:
        gbase: { value: 0.5, unit: nS }
        erev:  { value: 0, unit: mV }
        tau:   { value: 2, unit: ms }

integration:
  method: euler
  step_size: 0.005
  duration: 100.0
  time_scale: ms
""")
print(f"Model: {exp.dynamics.name if exp.dynamics else 'network'}")
print(f"Network nodes: {len(exp.network.nodes)}")

Model: hhcell_1
Network nodes: 5

2. Render LEMS XML

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

<Lems>
  <Target component="sim_NeuroML_Ex3__HH_Network"/>

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

    <ionChannelHH id="k" conductance="10 pS">
        <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="na" conductance="10 pS">
        <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>

    <ionChannelPassive id="passive" conductance="10 pS"/>

    <pointCellCondBased id="hhcell_1" C="10 pF" v0="-65 mV" thresh="20 mV">
        <channelPopulation id="k_pop" ionChannel="k" number="36000" erev="-77 mV"/>
        <channelPopulation id="na_pop" ionChannel="na" number="120000" erev="50 mV"/>
        <channelPopulation id="passive_pop" ionChannel="passive" number="300" erev="-54.3 mV"/>
    </pointCellCondBased>

    <ionChannelPassive id="leak" conductance="10 pS"/>

    <pointCellCondBased id="hhcell_2" C="10 pF" v0="-55 mV" thresh="20 mV">
        <channelPopulation id="leak_pop" ionChannel="leak" number="300" erev="-54.3 mV"/>
    </pointCellCondBased>

    <pulseGenerator id="pulseGenerator" amplitude="0.065 nA" delay="25.0 ms" duration="50.0 ms"/>

    <expOneSynapse id="expOneSynapse" erev="0.0 mV" gbase="0.5 nS" tauDecay="3.0 ms"/>

    <expTwoSynapse id="expTwoSynapse" erev="0.0 mV" gbase="0.5 nS" tauDecay="2.0 ms" tauRise="1.0 ms"/>

    <alphaSynapse id="alphaSynapse" erev="0.0 mV" gbase="0.5 nS" tau="2.0 ms"/>

    <network id="net1">
        <population id="hhcell_1_pop" component="hhcell_1" size="1"/>
        <population id="hhcell_2_pop" component="hhcell_2" size="3"/>
        <synapticConnection from="hhcell_1_pop[0]" to="hhcell_2_pop[0]" synapse="expOneSynapse" destination="synapses"/>
        <synapticConnection from="hhcell_1_pop[0]" to="hhcell_2_pop[1]" synapse="expTwoSynapse" destination="synapses"/>
        <synapticConnection from="hhcell_1_pop[0]" to="hhcell_2_pop[2]" synapse="alphaSynapse" destination="synapses"/>
        <explicitInput target="hhcell_1_pop[0]" input="pulseGenerator" destination="synapses"/>
    </network>

    <Simulation id="sim_NeuroML_Ex3__HH_Network" length="100.0ms" step="0.005ms" target="net1">
        <OutputFile id="of0" fileName="results/hhcell_2.dat">
            <OutputColumn id="hhcell_2_pop_0_v" qua

3. Run Reference

from tvbo.adapters.neuroml import run_lems_example

ref_outputs = run_lems_example("LEMS_NML2_Ex3_Net.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  ex3_v.dat: shape=(20001, 4)

4. Run TVBO

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

TVBO: ('time', 'quantity'), shape=(20001, 3)

5. Compare & Plot

from tvbo.adapters.neuroml import plot_lems_comparison
plot_lems_comparison("LEMS_NML2_Ex3_Net.xml", ref_outputs, result.integration.data, title_prefix="Ex3")