Ex13: Population Instances

Instance-based population with explicit cell placement — iafCell with pulse input

Model: Instance-Based Population

Explicit instance placement in NeuroML2. We model cell 0 (iafCells/0) which receives pulseGen1 (delay=100ms, duration=100ms, amplitude=0.3nA).

The iafCell equation: \(C\frac{dv}{dt} = g_L(E_L - v) + I_{\text{ext}}\)

---

1. Define iafCell in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex13: iafCell (Instances)"
dynamics:
  name: IaFCell
  parameters:
    tau:          { value: 20.0,  description: "C/gL = 1nF/50nS = 0.02s = 20ms" }
    leakReversal: { value: -60.0, description: "mV" }
    thresh:       { value: -55.0, description: "mV" }
    reset:        { value: -62.0, description: "mV" }
  derived_variables:
    I_ext:
      equation:
        rhs: "Piecewise((0.3, (t >= 0.1) & (t < 0.2)), (0.0, True))"
      description: "pulseGen1: 0.3nA / (1.0nF) = 0.3 mV/ms (times in SI seconds)"
  state_variables:
    v:
      equation:
        rhs: "(leakReversal - v) / tau + I_ext"
      initial_value: -60.0
      variable_of_interest: true
  events:
    spike:
      condition: { rhs: "v > thresh" }
      affect:    { rhs: "v = reset" }
network:
  number_of_nodes: 1
integration:
  method: euler
  step_size: 0.05
  duration: 300.0
  time_scale: ms
""")
print(f"Model: {exp.dynamics.name}")

Model: IaFCell

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_Ex13__iafCell__Instances_"/>

  <!-- ════════════════════════════════════════════════════════════════
       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"/>
  <Unit

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_Ex13_Instances.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  auto.dat: shape=(6001, 4)

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=(6001, 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))
    n_cols = min(ref_arr.shape[1] - 1, 10)
    for i in range(1, n_cols + 1):
        ax.plot(t, ref_arr[:, i] * 1000, alpha=0.7, label=f'Instance {i}')
    ax.set_xlabel("Time (ms)")
    ax.set_ylabel("Voltage (mV)")
    ax.set_title(f"Ex13: Instances — {name}")
    if n_cols <= 6:
        ax.legend(fontsize=7)
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.show()

Instance Placement

3D coordinates and instance-level connectivity are NeuroML-native. TVBO models the cell dynamics; network topology is expressed in NeuroML.