Ex14: PyNN Cells

PyNN-compatible cell types — IF_curr_alpha, IF_cond_exp, EIF_cond_exp_isfa_ista, HH_cond_exp

Model: PyNN Cell Types

NeuroML2 includes PyNN-compatible cell types, all expressible as ODEs:

• IF_curr_alpha: \(C\frac{dv}{dt} = -g_L(v - E_L) + I_{syn}\)
• IF_cond_exp: \(C\frac{dv}{dt} = -g_L(v - E_L) - g_{syn}(v - E_{syn})\)
• EIF_cond_exp: AdEx model (same as Ex8)
• HH_cond_exp: Hodgkin-Huxley (same as Ex1)

---

1. Define AdEx (EIF) in TVBO

from tvbo import SimulationExperiment

exp = SimulationExperiment.from_string("""
label: "NeuroML Ex14: PyNN AdEx Cell"
dynamics:
  name: AdaptiveExponentialIF
  parameters:
    C:      { value: 281.0 }
    gL:     { value: 30.0 }
    EL:     { value: -70.6 }
    VT:     { value: -50.4 }
    thresh: { value: -40.4 }
    reset:  { value: -48.5 }
    delT:   { value: 2.0 }
    tauw:   { value: 40.0 }
    a:      { value: 4.0 }
    b:      { value: 0.08 }
    I_ext:  { value: 0.8 }
  state_variables:
    v:
      equation:
        rhs: "(-gL*(v - EL) + gL*delT*exp((v - VT)/delT) - w + I_ext) / C"
      initial_value: -70.6
      variable_of_interest: true
    w:
      equation:
        rhs: "(a*(v - EL) - w) / tauw"
      initial_value: 0.0
  events:
    spike:
      condition: { rhs: "v > thresh" }
      affect:    { rhs: "v = reset; w = w + b" }
network:
  number_of_nodes: 1
integration:
  method: euler
  step_size: 0.025
  duration: 300.0
  time_scale: ms
""")
print(f"Model: {exp.dynamics.name}")

Model: AdaptiveExponentialIF

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_Ex14__PyNN_AdEx_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"/>
  <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"/>
  <Unit symb

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_Ex14_PyNN.xml")
for name, arr in ref_outputs.items():
    print(f"  {name}: shape={arr.shape}")

  ex14.dat: shape=(50001, 9)
  ex14_g.dat: shape=(50001, 3)

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}")

TVBO: shape=(12001, 3)

5. Plot All PyNN Variants (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], 6)):
        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"Ex14: PyNN Cells — {name}")
    ax.legend(fontsize=7)
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.show()

PyNN Cell Coverage

All four PyNN cell types are expressible as TVBO ODE systems: IaF (Ex0), conductance-based IaF, AdEx (Ex8), and HH (Ex1).