Ex12: Network 2

Larger IaF network with multiple populations

Model: IaF Network

A multi-population network of IaF cells with expTwoSynapse connections and pulse generator inputs. The individual cell dynamics are the same IaF model as Ex0.

1. Define IaF Cell in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex12: IaF Cell (Network 2)"
dynamics:
  name: IntegrateAndFire
  parameters:
    leakReversal: { value: -50.0 }
    tau:          { value: 30.0 }
    thresh:       { value: -55.0 }
    reset:        { value: -70.0 }
  state_variables:
    v:
      equation: { rhs: "(leakReversal - v) / tau" }
      initial_value: -50.0
      variable_of_interest: true
  events:
    spike:
      condition: { rhs: "v > thresh" }
      affect:    { rhs: "v = reset" }
network:
  number_of_nodes: 1
integration:
  method: euler
  step_size: 0.005
  duration: 200.0
  time_scale: ms
""")
print(f"Model: {exp.dynamics.name}")
Model: IntegrateAndFire

2. Render LEMS XML

xml = exp.render("lems")
print(xml[:1200])

<Lems>

  <!-- Tell jLEMS/jNeuroML which component is the simulation entry point. -->
  <Target component="sim_NeuroML_Ex12__IaF_Cell__Network_2_"/>

  <!-- ════════════════════════════════════════════════════════════════
       Dimensions & Units (inline — no external includes needed)
       ════════════════════════════════════════════════════════════════ -->

  <!-- Dimensions -->
  <Dimension name="none"/>
  <Dimension name="time" t="1"/>
  <Dimension name="voltage" m="1" l="2" t="-3" i="-1"/>
  <Dimension name="per_time" t="-1"/>
  <Dimension name="conductance" m="-1" l="-2" t="3" i="2"/>
  <Dimension name="capacitance" m="-1" l="-2" t="4" i="2"/>
  <Dimension name="current" i="1"/>
  <Dimension name="resistance" m="1" l="2" t="-3" i="-2"/>
  <Dimension name="concentration" l="-3" n="1"/>
  <Dimension name="substance" n="1"/>
  <Dimension name="charge" t="1" i="1"/>
  <Dimension name="temperature" k="1"/>

  <!-- Units -->
  <Unit symbol="s" dimension="time" power="0"/>
  <Unit symbol="ms" dimension="time" power="-3"/>
  <Unit symbol="us" dimension="time" power="-6"/>
  <Unit symbol="V" dimension="voltage" power="0"/>
  <Unit symbol="mV" dimension="voltage" power="-3"/>
  <Uni

3. Run Reference

import sys, os
sys.path.insert(0, os.path.dirname(os.path.abspath(".")))
from _nml_helpers import run_lems_example

ref_outputs = run_lems_example("LEMS_NML2_Ex12_Net2.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")
  ex12.dat: shape=(60001, 10)

4. Run TVBO

import numpy as np

result = exp.run("neuroml")
da = result.integration.data
tvbo_arr = np.column_stack([da.coords['time'].values, da.values])
print(f"TVBO: shape={tvbo_arr.shape}")
TVBO: shape=(40001, 2)

5. Plot Reference Network

import matplotlib.pyplot as plt
import numpy as np

for name, ref_arr in ref_outputs.items():
    t = ref_arr[:, 0] * 1000
    fig, ax = plt.subplots(figsize=(10, 4))
    for i in range(1, min(ref_arr.shape[1], 8)):
        ax.plot(t, ref_arr[:, i] * 1000, alpha=0.7, label=f'Cell {i}')
    ax.set_xlabel("Time (ms)")
    ax.set_ylabel("Voltage (mV)")
    ax.set_title(f"Ex12: Network 2 — {name}")
    ax.legend(fontsize=7)
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.show()

Network Topology

The multi-population connectivity and synaptic projections are NeuroML-native. TVBO models the individual cell dynamics.