Model: Fractional Conductance
Demonstrates <gateFractional> — ion channels where the gate exponent is non-integer (e.g., \(m^{1.5}\) instead of \(m^3\) ), useful for fitting experimental data from specific brain regions.
The underlying model is Hodgkin-Huxley with modified gate powers.
1. Define HH Cell in TVBO
from tvbo import SimulationExperiment
exp = SimulationExperiment.from_string("""
label: "NeuroML Ex24: HH with Fractional Gates"
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: 300.0
time_scale: ms
""" )
print (f"Model: { exp. dynamics. name} " )
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_Ex24__HH_with_Fractional_Gates"/>
<!-- ════════════════════════════════════════════════════════════════
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" power="-3"/>
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_Ex24_FractionalConductance.xml" )
for name, arr in ref_outputs.items():
print (f" { name} : shape= { arr. shape} " )
ex24_k.dat: shape=(20001, 2)
ex24_v.dat: shape=(20001, 2)
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} " )
5. Plot Reference
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 ))
for i in range (1 , min (ref_arr.shape[1 ], 4 )):
ax.plot(t, ref_arr[:, i] * 1000 , alpha= 0.8 , label= f'Cell { i} ' )
ax.set_xlabel("Time (ms)" )
ax.set_ylabel("Voltage (mV)" )
ax.set_title(f"Ex24: Fractional Conductance — { name} " )
ax.legend(fontsize= 7 )
ax.grid(True , alpha= 0.3 )
plt.tight_layout()
plt.show()
Non-integer gate exponents (\(m^{1.5}\) ) are directly expressible in TVBO using **1.5 in the voltage equation. No special syntax is needed.