Ex16: Input Types

Various input types — Izhikevich 2007 RS cell with pulse generator

Model: Input Types

Demonstrates NeuroML2 input generator types applied to Izhikevich 2007 RS cells. We model cell 0 (izhPop[0]) which receives pulseGen0 (delay=50ms, duration=200ms, amplitude=1nA).

The Izhikevich 2007 model equations:

\[C\frac{dv}{dt} = k(v - v_r)(v - v_t) + I_{\text{ext}} - u\] \[\frac{du}{dt} = a(b(v - v_r) - u)\]

---

1. Define Izhikevich 2007 RS Cell in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex16: Izhikevich 2007 RS with Pulse Input"
dynamics:
  name: Izhikevich2007Cell
  parameters:
    C:     { value: 100.0,  description: "Capacitance (pF)" }
    k:     { value: 0.7,    description: "nS/mV" }
    vr:    { value: -60.0,  description: "Resting potential (mV)" }
    vt:    { value: -40.0,  description: "Threshold potential (mV)" }
    vpeak: { value: 35.0,   description: "Peak voltage (mV)" }
    a:     { value: 0.03,   description: "Recovery rate (1/ms)" }
    b:     { value: -2.0,   description: "Recovery sensitivity (nS)" }
    c:     { value: -50.0,  description: "Reset voltage (mV)" }
    d:     { value: 100.0,  description: "Recovery increment (pA)" }
  derived_variables:
    I_ext:
      equation:
        rhs: "Piecewise((1000.0, (t >= 0.05) & (t < 0.25)), (0.0, True))"
      description: "pulseGen0: delay=50ms, duration=200ms, amplitude=1nA=1000pA (times in SI seconds)"
  state_variables:
    v:
      equation:
        rhs: "(k*(v - vr)*(v - vt) + I_ext - u) / C"
      initial_value: -60.0
      variable_of_interest: true
    u:
      equation:
        rhs: "a*(b*(v - vr) - u)"
      initial_value: 0.0
  events:
    spike:
      condition: { rhs: "v > vpeak" }
      affect:    { rhs: "v = c; u = u + d" }
network:
  number_of_nodes: 1
integration:
  method: euler
  step_size: 0.001
  duration: 300.0
  time_scale: ms
""")
print(f"Model: {exp.dynamics.name}")

Model: Izhikevich2007Cell

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_Ex16__Izhikevich_2007_RS_with_Pulse_Input"/>

  <!-- ════════════════════════════════════════════════════════════════
       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" powe

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_Ex16_Inputs.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  ex16_v.dat: shape=(300001, 11)

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=(300001, 3)

5. Plot Reference (All Input Types)

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_show = min(ref_arr.shape[1] - 1, 8)
    for i in range(1, n_show + 1):
        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"Ex16: Input Types — {name}")
    ax.legend(fontsize=6, ncol=2)
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.show()

Input Types in TVBO

The various input generators (pulse, sine, ramp, voltage clamp, spike generators) are NeuroML-specific network features. TVBO represents the cell dynamics; the input waveforms are handled by the NeuroML adapter during rendering.