NML2: Full NeuroML

Complete NeuroML document with cell, channels, network, and inputs

NML2_FullNeuroML.nml

A comprehensive example combining all major NeuroML elements in a single file:

• Ion channels (Na, K, passive)
• Cell with morphology and biophysical properties
• Network with populations and projections
• Explicit input (pulse generator)

from pathlib import Path

nml_file = Path.home() / "work_data/toolboxes/NeuroML2/examples/NML2_FullNeuroML.nml"
text = nml_file.read_text()

# Count element types
import re
elements = re.findall(r'<(\w+)\s', text)
from collections import Counter
counts = Counter(elements)
for el, count in counts.most_common(20):
    print(f"  <{el}>: {count}")

  <neuroml>: 1
  <morphology>: 1
  <segment>: 1
  <proximal>: 1
  <distal>: 1
  <segmentGroup>: 1
  <member>: 1
  <ionChannelHH>: 1
  <expTwoSynapse>: 1
  <biophysicalProperties>: 1
  <channelDensity>: 1
  <spikeThresh>: 1
  <specificCapacitance>: 1
  <initMembPotential>: 1
  <resistivity>: 1
  <cell>: 1
  <network>: 1
  <population>: 1
  <projection>: 1

Document Structure

# Show top-level elements
for line in text.split('\n'):
    stripped = line.strip()
    # Only show opening tags with id= or name=, at first indent levels
    indent = len(line) - len(line.lstrip())
    if indent <= 4 and stripped.startswith('<') and not stripped.startswith('<!') and \
       not stripped.startswith('</') and not stripped.startswith('<?'):
        print(stripped[:100])

<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
<morphology id="NeuroMorpho_PyrCell123">  <!-- see FullCell.xml for more details -->
<ionChannelHH id="HH_Na" conductance="10pS" species="na">  <!-- see SimpleIonChannel.xml for more de
<expTwoSynapse id="AMPA" gbase="0.5nS" erev="0mV" tauRise="1ms" tauDecay="2ms" />
<biophysicalProperties id="PyrCellChanDist">
<cell id="PyrCell"
<network id="PyrCellNet">

TVBO Representation: HH Cell

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NML2 FullNeuroML: HH"
dynamics:
  name: HodgkinHuxley
  parameters:
    C:     { value: 10.0 }
    g_Na:  { value: 1200.0 }
    g_K:   { value: 360.0 }
    g_L:   { value: 3.0 }
    E_Na:  { value: 50.0 }
    E_K:   { value: -77.0 }
    E_L:   { value: -54.3 }
    I_ext: { value: 0.08 }
  derived_variables:
    alpha_m:
      equation:
        rhs: "Piecewise((1.0, Eq(v, -40.0)), (0.1*(v + 40.0)/(1.0 - exp(-(v + 40.0)/10.0)), True))"
    beta_m:
      equation: { rhs: "4.0*exp(-(v + 65.0)/18.0)" }
    alpha_h:
      equation: { rhs: "0.07*exp(-(v + 65.0)/20.0)" }
    beta_h:
      equation: { rhs: "1.0/(1.0 + exp(-(v + 35.0)/10.0))" }
    alpha_n:
      equation:
        rhs: "Piecewise((0.1, Eq(v, -55.0)), (0.01*(v + 55.0)/(1.0 - exp(-(v + 55.0)/10.0)), True))"
    beta_n:
      equation: { rhs: "0.125*exp(-(v + 65.0)/80.0)" }
  state_variables:
    v:
      equation:
        rhs: "(-g_Na*m**3*h*(v - E_Na) - g_K*n**4*(v - E_K) - g_L*(v - E_L) + I_ext*1000) / C"
      initial_value: -65.0
      variable_of_interest: true
    m:
      equation: { rhs: "alpha_m*(1 - m) - beta_m*m" }
      initial_value: 0.05
    h:
      equation: { rhs: "alpha_h*(1 - h) - beta_h*h" }
      initial_value: 0.6
    n:
      equation: { rhs: "alpha_n*(1 - n) - beta_n*n" }
      initial_value: 0.32
network:
  number_of_nodes: 1
integration:
  method: euler
  step_size: 0.01
  duration: 150.0
  time_scale: ms
""")
xml = exp.render("lems")
print(xml[:800])

<Lems>

  <!-- Tell jLEMS/jNeuroML which component is the simulation entry point. -->
  <Target component="sim_NML2_FullNeuroML__HH"/>

  <!-- ════════════════════════════════════════════════════════════════
       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"/>
  <Dimens

Run TVBO

import numpy as np
import matplotlib.pyplot as plt

result = exp.run("neuroml")
da = result.integration.data
t = da.coords['time'].values
v = da.values[:, 0]

fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(t, v)
ax.set_xlabel("Time (ms)")
ax.set_ylabel("Voltage (mV)")
ax.set_title("NML2 FullNeuroML: HH Cell via TVBO")
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()