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The RE comparison process has been updated.
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‎NeuroML2/compare_MC/RE/README.md‎

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`auto_compare.sh` provides the following modes:
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- `--pas-nrn`
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NeuroML vs NEURON, passive (pas-only/leaky) cell.
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- `--hh2-nrn`
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NeuroML vs NEURON, original mechanism (`HH2.mod` → `hh2ad`).
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- `--hh2-cnexp-nrn`
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NeuroML vs NEURON, hh2 with cnexp mechanisms (`hh2_na_cnexp.mod` + `hh2_k_cnexp.mod`, `SUFFIX hh2_na/hh2_k`).
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- `--hh2-hh2ad-vs-split`
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NEURON vs NEURON, direct comparison of `hh2ad` (HH2.mod) vs split `hh2_na+hh2_k` on the same cell.
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Usage example (from this directory):
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```bash
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chmod +x auto_compare.sh
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./auto_compare.sh --pas-nrn
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./auto_compare.sh --hh2-nrn
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./auto_compare.sh --hh2-cnexp-nrn
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./auto_compare.sh --hh2-hh2ad-vs-split
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```
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Notes:
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- The NeuroML-based scripts (`run_nml_hh2.py`, `run_nml_leaky.py`, `omv_test.py`) will automatically set `NEURON_HOME` if it is not already defined, by locating `nrniv` on your `PATH`.
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In the mod folder, there are the original file HH2.mod, the files hh2_k.mod and hh2_na.mod exported from NeuroML, as well as the modified mod files mod/hh2_na_cnexp.mod and mod/hh2_k_cnexp.mod (the purpose is to demonstrate why the mod files exported from NeuroML cannot be recognized as cnexp format). By using nrnivmodl mod to check the C files, the differences can be observed.
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Result: The difference in the leak channel is 0. After incorporating hh2ad, there is a significant numerical discrepancy during action potential generation. While the discrepancy for a single channel may be acceptable, when all ion channels are combined, it leads to the generation of an additional spike.

‎NeuroML2/compare_MC/RE/RE_hh2_cell.nml‎

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<neuroml xmlns="http://www.neuroml.org/schema/neuroml2" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2.3.1.xsd" id="RE_hh2">
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<notes>NeuroML cell with only hh2_na/hh2_k and passive leak</notes>
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<include href="hh2_na.channel.nml"/>
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<include href="hh2_k.channel.nml"/>
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<include href="pas.channel.nml"/>
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<cell id="RE_hh2_cell">
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<notes>Single-compartment soma</notes>
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<morphology id="morphology">
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<segment id="0" name="Seg0_soma">
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<proximal x="0.0" y="0.0" z="0.0" diameter="70.0"/>
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<distal x="0.0" y="64.86" z="0.0" diameter="70.0"/>
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</segment>
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</morphology>
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<biophysicalProperties id="biophys">
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<membraneProperties>
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<channelDensity id="pas_soma" ionChannel="pas" condDensity="5e-5 S_per_cm2" erev="-77.0 mV" ion="non_specific"/>
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<channelDensity ionChannel="hh2_k" id="hh2_k_soma" ion="k" erev="-95.0 mV" condDensity="0.01 S_per_cm2"/>
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<channelDensity ionChannel="hh2_na" id="hh2_na_soma" ion="na" erev="50.0 mV" condDensity="0.09 S_per_cm2"/>
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<spikeThresh value="5mV"/>
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<specificCapacitance value="1 uF_per_cm2"/>
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<initMembPotential value="-70mV"/>
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</membraneProperties>
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<intracellularProperties>
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<resistivity value="100 ohm_cm"/>
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</intracellularProperties>
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</biophysicalProperties>
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</cell>
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</neuroml>
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‎NeuroML2/compare_MC/RE/RE_netpy.py‎

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import os
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# Force headless plotting for Matplotlib
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os.environ["MPLBACKEND"] = "Agg"
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import matplotlib
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matplotlib.use("Agg")
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# Ensure NEURON runs headless in non-GUI environments
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os.environ["NEURON_NO_GUI"] = "1"
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os.environ.pop("DISPLAY", None)
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print(f"[RE_netpy] Headless env: DISPLAY={os.environ.get('DISPLAY')}, NEURON_NO_GUI={os.environ.get('NEURON_NO_GUI')}, MPLBACKEND={os.environ.get('MPLBACKEND')}")
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from netpyne import specs, sim
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import pprint
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soma['ions']['na'] = {'e': 50.0, 'i': 10.0, 'o': 140.0}
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soma['mechs']['pas'] = {'g': 5e-5, 'e': -77}
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soma['mechs']['cadad'] = {'cainf': 0.00024, 'depth': 1, 'kd': 0.0, 'kt': 0.0, 'taur': 5}
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# soma['mechs']['kl'] = {'gmax': 3e-06}
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# soma['mechs']['itre'] = {"gmax": 0.002,"shift": 2.0}
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soma['mechs']['kl'] = {'gmax': 3e-06}
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soma['mechs']['itre'] = {"gmax": 0.002,"shift": 2.0}
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soma['mechs']['hh2ad'] = {"gkbar": 0.01,"gnabar": 0.09,"vtraub": -50.0}
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netParams.popParams['RE'] = {'cellType': 'RE_HH_reduced', 'numCells': 1}
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netParams.stimSourceParams['Input'] = {'type': 'IClamp', 'dur': 500, 'del': 200, 'amp': 0.3}
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netParams.stimSourceParams['Input'] = {'type': 'IClamp', 'dur': 500, 'del': 200, 'amp': 0.03}
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netParams.stimTargetParams['Input->RE'] = {'source': 'Input', 'sec': 'soma', 'loc': 0.5, 'conds': {'cellType': 'RE_HH_reduced'}}
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netParams.defaultThreshold = 5.0
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# Simulation options
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simConfig.recordCells = ['all']
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simConfig.hParams['celsius'] = 34
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simConfig.duration = 1000 # Duration of the simulation, in ms
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simConfig.dt = 0.001
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simConfig.dt = 0.025
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# Internal integration timestep to use
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simConfig.verbose = True # Show detailed messages
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simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'},
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simConfig.recordTraces = {
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# Membrane
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'V_soma':{'sec':'soma','loc':0.5,'var':'v'},
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'cai':{'sec':'soma','loc':0.5,'var':'cai'},
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# HH Na/K (Traub hh2ad)
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'm_hh2_na':{'sec': 'soma', 'loc': 0.5, 'mech': 'hh2ad', 'var': 'm'},
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'h_hh2_na':{'sec': 'soma', 'loc': 0.5, 'mech': 'hh2ad', 'var': 'h'},
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'n_hh2_k':{'sec': 'soma', 'loc': 0.5, 'mech': 'hh2ad', 'var': 'n'},
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'n_hh2_k':{'sec': 'soma', 'loc': 0.5, 'mech': 'hh2ad', 'var': 'n'},
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# T type Ca channel (itre) from IT2.mod: states m,h; mechanism current 'i'
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'm_itre':{'sec': 'soma', 'loc': 0.5, 'mech': 'itre', 'var': 'm'},
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'h_itre':{'sec': 'soma', 'loc': 0.5, 'mech': 'itre', 'var': 'h'},
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'i_itre':{'sec': 'soma', 'loc': 0.5, 'mech': 'itre', 'var': 'i'},
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# Passive leak K channel current uses variable 'i' in kl.mod
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'i_kl':{'sec': 'soma', 'loc': 0.5, 'mech': 'kl', 'var': 'i'},
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# Total ionic currents for reference
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'ina':{'sec': 'soma', 'loc': 0.5, 'var': 'ina'},
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'ik':{'sec': 'soma', 'loc': 0.5, 'var': 'ik'}} # Dict with traces to record
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'ik':{'sec': 'soma', 'loc': 0.5, 'var': 'ik'},
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'ica':{'sec': 'soma', 'loc': 0.5, 'var': 'ica'}
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} # Dict with traces to record
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simConfig.recordStep = 0.001 # Step size in ms to save data (eg. V traces, LFP, etc)
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simConfig.filename = 'RE_reduced_hh2' # Set file output name
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simConfig.recordStep = 0.025 # Match integration step for high‑res alignment
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simConfig.filename = 'RE_full_data' # Set file output name
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simConfig.savePickle = False # Save params, network and sim output to pickle file
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simConfig.saveDataInclude = ['simData']
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simConfig.saveJson = True
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simConfig.analysis['plotTraces'] = {'include': [0], 'saveFig': True} # Plot recorded traces for this list of cells
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simConfig.analysis['plotRaster'] = {'saveFig': True} # Plot a raster
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# 以 NetPyNE 默认方式保存 JSON（兼容不同版本）
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simConfig.saveJson = True
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# Disable built-in plotting during headless runs to avoid DISPLAY issues
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simConfig.analysis = {}
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# Create network and run simulation
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sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)
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#import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty
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#import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty

‎NeuroML2/compare_MC/RE/RE_reduced_cell.nml‎

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</morphology>
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<biophysicalProperties id="biophys">
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<membraneProperties>
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<channelDensityNernst id="itre_soma" ionChannel="itre" condDensity="0.002 S_per_cm2" ion="ca"/>
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<channelDensity ionChannel="pas" id="pas_soma" ion="non_specific" erev="-77.0 mV" condDensity="5e-5 S_per_cm2"/>
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<channelDensity ionChannel="kl" id="kl_soma" ion="k" erev="-95.0 mV" condDensity="3e-06 S_per_cm2"/>
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<channelDensity ionChannel="hh2_k" id="hh2_k_soma" ion="k" erev="-95.0 mV" condDensity="0.01 S_per_cm2"/>
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<channelDensity ionChannel="hh2_na" id="hh2_na_soma" ion="na" erev="50.0 mV" condDensity="0.09 S_per_cm2"/>
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<spikeThresh value="5mV"/>
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<specificCapacitance value="1 uF_per_cm2"/>
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<initMembPotential value="-70mV"/>

‎NeuroML2/compare_MC/RE/RE_reduced_cellParams.json‎

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‎NeuroML2/compare_MC/RE/Voltage_all_channels.png‎

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‎NeuroML2/compare_MC/RE/auto_compare.sh‎

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#!/usr/bin/env bash
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set -euo pipefail
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usage() {
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cat <<'USAGE'
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Usage: ./auto_compare.sh [--pas-nrn | --hh2-nrn | --hh2-cnexp-nrn | --hh2-hh2ad-vs-split] [--help]
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Options:
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--pas-nrn Run passive (pas) NEURON (Python) vs NeuroML comparison
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--hh2-nrn Run hh2 (Traub Na/K, HH2.mod) NEURON (Python) vs NeuroML comparison
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--hh2-cnexp-nrn Run hh2 (Na/K split cnexp: hh2_na_cnexp/hh2_k_cnexp) NEURON (Python) vs NeuroML comparison
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--hh2-hh2ad-vs-split Run NEURON hh2ad (HH2.mod) vs split hh2_na+hh2_k comparison
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-h, --help Show this help
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USAGE
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}
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run_hh2_nrn() {
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echo "[NRN-HH2 1/4] Compile mechanisms from mod/ (HH2.mod -> hh2ad)"
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rm -rf x86_64 || true
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nrnivmodl mod
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echo "[NRN-HH2 2/4] Running pure NEURON (Python) hh2 (dt=0.025 ms)"
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python3 run_neuron_hh2.py
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echo "[NRN-HH2 3/4] Running NeuroML hh2 (dt=0.025 ms)"
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python3 run_nml_hh2.py
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echo "[NRN-HH2 4/4] Comparing traces (high-res, step-aligned)"
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python3 compare_neuron_nml_hh2.py
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echo "[NRN-HH2] Done. See compare_out/hh2_neuron_vs_nml for outputs."
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}
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run_hh2_cnexp_nrn() {
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echo "[NRN-HH2-CNEXP 1/4] Compile mechanisms from mod/ (hh2_na_cnexp.mod, hh2_k_cnexp.mod, HH2.mod)"
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rm -rf x86_64 || true
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nrnivmodl mod
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echo "[NRN-HH2-CNEXP 2/4] Running pure NEURON (Python) hh2 cnexp split (dt=0.025 ms)"
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python3 run_neuron_hh2_cnexp.py
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echo "[NRN-HH2-CNEXP 3/4] Running NeuroML hh2 (dt=0.025 ms)"
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python3 run_nml_hh2.py
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echo "[NRN-HH2-CNEXP 4/4] Comparing traces (high-res, step-aligned)"
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python3 compare_neuron_nml_hh2_cnexp.py
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echo "[NRN-HH2-CNEXP] Done. See compare_out/hh2_cnexp_neuron_vs_nml for outputs."
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}
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run_pas_nrn() {
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echo "[NRN-PAS 1/3] Running pure NEURON (Python) passive (dt=0.025 ms)"
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python3 run_neuron_pas.py
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echo "[NRN-PAS 2/3] Running NeuroML leaky (dt=0.025 ms)"
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python3 run_nml_leaky.py
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echo "[NRN-PAS 3/3] Comparing traces (high-res, step-aligned)"
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python3 compare_neuron_nml_pas.py
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echo "[NRN-PAS] Done. See compare_out/leaky_neuron_vs_nml for outputs."
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}
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run_hh2_hh2ad_vs_split() {
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echo "[RE hh2 hh2ad vs (hh2_na+hh2_k) 1/4] Compiling NEURON mechanisms (mod/hh2_na.mod, mod/hh2_k.mod, HH2.mod, pas_nml2.mod, etc.)"
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rm -rf x86_64 || true
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nrnivmodl mod
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echo "[RE hh2 hh2ad vs (hh2_na+hh2_k) 2/4] Running NEURON hh2ad (HH2.mod, dt=0.025 ms)"
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python3 run_neuron_hh2.py
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echo "[RE hh2 hh2ad vs (hh2_na+hh2_k) 3/4] Running NEURON split hh2 (hh2_na+hh2_k, dt=0.025 ms)"
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python3 run_neuron_hh2_split.py
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echo "[RE hh2 hh2ad vs (hh2_na+hh2_k) 4/4] Comparing mechanisms and generating plots"
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python3 compare_neuron_hh2_hh2ad_vs_split.py
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echo "[RE hh2 hh2ad vs (hh2_na+hh2_k)] Done. See compare_out/hh2_hh2ad_vs_split_neuron for results."
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}
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main() {
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case "${1-}" in
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--pas-nrn)
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run_pas_nrn
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;;
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--hh2-nrn)
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run_hh2_nrn
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;;
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--hh2-cnexp-nrn)
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run_hh2_cnexp_nrn
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;;
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--hh2-hh2ad-vs-split)
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run_hh2_hh2ad_vs_split
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;;
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-h|--help|"")
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usage
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;;
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*)
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echo "Unknown option: $1" >&2
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usage
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exit 1
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;;
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esac
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}
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main "$@"

‎NeuroML2/compare_MC/RE/compare/hh2ad/compare_hh2.png‎

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‎NeuroML2/compare_MC/RE/compare/itre/compare_itre.png‎

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‎NeuroML2/compare_MC/RE/compare/kl/Figure_1.png‎

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