NML2_SimpleIonChannel.nml
Minimal examples of <ionChannelHH> with <gateHHrates>:
- Forward rate:
HHExpLinearRate, HHExpRate, HHSigmoidRate
- Reverse rate: same types with different parameters
from pathlib import Path
nml_file = Path.home() / "work_data/toolboxes/NeuroML2/examples/NML2_SimpleIonChannel.nml"
text = nml_file.read_text()
# Show channel definitions
for line in text.split('\n'):
stripped = line.strip()
if '<ionChannel' in stripped or '<gate' in stripped or \
'<forwardRate' in stripped or '<reverseRate' in stripped or \
'</ionChannel' in stripped:
print(stripped[:120])
<ionChannelHH id="NaConductance" conductance="10pS" species="na">
<gateHHrates id="m" instances="3">
<forwardRate type="HHExpLinearRate" rate="1per_ms" midpoint="-40mV" scale="10mV"/>
<reverseRate type="HHExpRate" rate="4per_ms" midpoint="-65mV" scale="-18mV"/>
<gateHHrates id="h" instances="1">
<forwardRate type="HHExpRate" rate="0.07per_ms" midpoint="-65mV" scale="-20mV"/>
<reverseRate type="HHSigmoidRate" rate="1per_ms" midpoint="-35mV" scale="10mV"/>
</ionChannelHH>
TVBO Representation: HH Cell (Using These Channels)
from tvbo import SimulationExperiment
exp = SimulationExperiment.from_string("""
label: "NML2 SimpleIonChannel: 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_SimpleIonChannel__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"/>
<D
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 SimpleIonChannel: HH Cell via TVBO")
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()
