NML2: Full Cell

Complete cell with morphology, channels, and biophysical properties

NML2_FullCell.nml

Combines morphology and biophysics in a complete cell definition, ready for multi-compartment simulation. Includes:

• Detailed morphology with soma, axon, and dendritic segments
• Channel densities assigned to segment groups
• Specific capacitance and initial membrane potential

import urllib.request

url = "https://raw.githubusercontent.com/NeuroML/NeuroML2/master/examples/NML2_FullCell.nml"
with urllib.request.urlopen(url) as resp:
    text = resp.read().decode()

# Count segments and channels
import re
n_segments = len(re.findall(r'<segment ', text))
n_channels = len(re.findall(r'<channelDensity', text))
n_groups = len(re.findall(r'<segmentGroup', text))
print(f"Segments: {n_segments}, Channel densities: {n_channels}, Segment groups: {n_groups}")

# Show cell structure
for line in text.split('\n'):
    stripped = line.strip()
    if any(tag in stripped for tag in ['<cell ', '<morphology', '<biophysical',
           '<channelDensity', '<specificCapacitance', '</cell>']):
        print(stripped[:120])

Segments: 4, Channel densities: 2, Segment groups: 3
<cell id="SpikingCell" metaid="HippoCA1Cell">
<morphology id="SpikingCell_morphology">
<biophysicalProperties id="bio_cell">
<channelDensity id="pasChans" ionChannel="pas" condDensity="3.0 S_per_m2" erev="-70mV" ion="non_specific"/> <!-- no segm
<channelDensity id="naChansSoma" ionChannel="NaConductance" segmentGroup="soma_group" condDensity="120.0 mS_per_cm2" ere
<specificCapacitance segmentGroup="soma_group" value="1.0 uF_per_cm2"/>
<specificCapacitance segmentGroup="dendrite_group" value="2.0 uF_per_cm2"/>
</cell>

TVBO Representation: HH Cell

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NML2 FullCell: 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_FullCell__HH"/>

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

  <!-- ════════════════════════════════════════════════════════════════
       Dynamics ComponentType & Component instances
       ════════════════════════════════════════════════════════════════ -->

  <!-- ════════════════════════════════════════════════════════════════
       ComponentType: HodgkinHuxley
       Generated from TVBO Dynamics: HodgkinHuxley
       ════════════════════════════════════════════════════════════════ -->
  <ComponentType name="HodgkinHuxley">

    <!-- Parameters -->
    <Parameter name="C" dimension="none"/>
    <Parameter name="E_K

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 FullCell: HH Cell via TVBO")
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()