Ex13: Population Instances

Instance-based population with explicit cell placement — 3 iafCells, 2 projections, pulse input

Model: Instance-Based Network

Three iafCell instances with explicit 3D placement. Cell 0 receives a pulse input and connects to cells 1 and 2 via two different expOneSynapse connections.

Reference: NML2_InstanceBasedNetwork.nml

---

1. Define Network in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex13: Instance-Based Network"
dynamics:
  name: iaf
  iri: neuroml:iafCell
  parameters:
    leakReversal:    { value: -60, unit: mV }
    thresh:          { value: -55, unit: mV }
    reset:           { value: -62, unit: mV }
    C:               { value: 1.0, unit: nF }
    leakConductance: { value: 0.05, unit: uS }

network:
  number_of_nodes: 4
  dynamics:
    iaf:
      name: iaf
      iri: neuroml:iafCell
      parameters:
        leakReversal:    { value: -60, unit: mV }
        thresh:          { value: -55, unit: mV }
        reset:           { value: -62, unit: mV }
        C:               { value: 1.0, unit: nF }
        leakConductance: { value: 0.05, unit: uS }
    pulseGen1:
      name: pulseGen1
      iri: neuroml:pulseGenerator
      parameters:
        delay:     { value: 100, unit: ms }
        duration:  { value: 100, unit: ms }
        amplitude: { value: 0.3, unit: nA }
  nodes:
    - id: 0
      dynamics: iaf
    - id: 1
      dynamics: iaf
    - id: 2
      dynamics: iaf
    - id: 100
      dynamics: pulseGen1
  edges:
    - source: 100
      target: 0
    - source: 0
      target: 1
      coupling: expOneSynapse
      parameters:
        gbase:    { value: 5, unit: nS }
        erev:     { value: 0, unit: mV }
        tauDecay: { value: 3, unit: ms }
    - source: 0
      target: 2
      coupling: expOneSynapse
      parameters:
        gbase:    { value: 10, unit: nS }
        erev:     { value: 0, unit: mV }
        tauDecay: { value: 2, unit: ms }

integration:
  method: euler
  step_size: 0.05
  duration: 300.0
  time_scale: ms
""")
print(f"Model: {exp.dynamics.name if exp.dynamics else 'network'}")
print(f"Network nodes: {len(exp.network.nodes)}")

Model: iaf
Network nodes: 4

2. Render LEMS XML

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

<Lems>
  <Target component="sim_NeuroML_Ex13__Instance_Based_Network"/>

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

    <iafCell id="iaf" C="1 nF" leakConductance="0.05 uS" leakReversal="-60 mV" reset="-62 mV" thresh="-55 mV"/>

    <pulseGenerator id="pulseGen1" amplitude="0.3 nA" delay="100.0 ms" duration="100.0 ms"/>

    <expOneSynapse id="expOneSynapse" erev="0.0 mV" gbase="5.0 nS" tauDecay="3.0 ms"/>

    <expOneSynapse id="expOneSynapse_2" erev="0.0 mV" gbase="10.0 nS" tauDecay="2.0 ms"/>

    <network id="net1">
        <population id="iaf_pop" component="iaf" size="3"/>
        <synapticConnection from="iaf_pop[0]" to="iaf_pop[1]" synapse="expOneSynapse" destination="synapses"/>
        <synapticConnection from="iaf_pop[0]" to="iaf_pop[2]" synapse="expOneSynapse_2" destination="synapses"/>
        <explicitInput target="iaf_pop[0]" input="pulseGen1" destination="synapses"/>
    </network>

    <Simulation id="sim_NeuroML_Ex13__Instance_Based_Network" length="300.0ms" step="0.05ms" target="net1">
        <OutputFile id="of0" fileName="results/iaf.dat">
            <OutputColumn id="iaf_pop_0_v" quantity="iaf_pop[0]/v"/>
            <OutputColumn id="iaf_pop_1_v" quantity="iaf_pop[1]/v"/>
            <OutputColumn id="iaf_pop_2_v" quantity="iaf_pop[2]/v"/>
        </OutputFile>
    </Simulation>

</Lems>

3. Run Reference

from tvbo.adapters.neuroml import run_lems_example

ref_outputs = run_lems_example("LEMS_NML2_Ex13_Instances.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  auto.dat: shape=(6001, 4)

4. Run TVBO

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

TVBO: ('time', 'quantity'), shape=(6001, 3)

5. Compare & Plot

from tvbo.adapters.neuroml import plot_lems_comparison
plot_lems_comparison("LEMS_NML2_Ex13_Instances.xml", ref_outputs, result.integration.data, title_prefix="Ex13")