NML2_GapJunctions.nml
Defines <gapJunction> elements for bidirectional electrical coupling:
<gapJunction id="gj1" conductance="10pS"/>
Connected via <electricalConnection> in the network.
from pathlib import Path
nml_file = Path.home() / "work_data/toolboxes/NeuroML2/examples/NML2_GapJunctions.nml"
text = nml_file.read_text()
for line in text.split('\n'):
stripped = line.strip()
if '<gapJunction' in stripped or '<electrical' in stripped or \
'<network' in stripped or '<population' in stripped:
print(stripped[:120])
<gapJunction id="gj1" conductance="10pS"/>
<network id="net1">
<population id="iafPop1" component="iaf" size="1" />
<population id="iafPop2" component="iaf" size="1" />
<electricalProjection id ="testGJconn" presynapticPopulation="iafPop1" postsynapticPopulation="iafPop2">
<electricalConnection id="0" preCell="0" postCell="0" synapse="gj1"/>
TVBO Representation: FHN Cell
from tvbo import SimulationExperiment
exp = SimulationExperiment.from_string("""
label: "NML2 GapJunctions: FHN Cell"
dynamics:
name: FitzHughNagumo
parameters:
I: { value: 0.8 }
state_variables:
V:
equation: { rhs: "V - V**3/3 - W + I" }
initial_value: 0.0
variable_of_interest: true
W:
equation: { rhs: "0.08*(V + 0.7 - 0.8*W)" }
initial_value: 0.0
network:
number_of_nodes: 1
integration:
method: euler
step_size: 0.01
duration: 200.0
time_scale: s
""")
xml = exp.render("lems")
print(xml[:800])
<Lems>
<!-- Tell jLEMS/jNeuroML which component is the simulation entry point. -->
<Target component="sim_NML2_GapJunctions__FHN_Cell"/>
<!-- ════════════════════════════════════════════════════════════════
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"/>
Run TVBO
import numpy as np
import matplotlib.pyplot as plt
result = exp.run("neuroml")
da = result.integration.data
t = da.coords['time'].values
vals = da.values
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(t, vals[:, 0], label='V')
ax.plot(t, vals[:, 1], label='W', alpha=0.7)
ax.set_xlabel("Time (s)")
ax.set_title("NML2 GapJunctions: FHN Cell via TVBO")
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()
