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javascript: Promise.all([import('https://unpkg.com/turndown@6.0.0?module'), import('https://unpkg.com/@tehshrike/readability@0.2.0'), ]).then(async ([{
    default: Turndown
}, {
    default: Readability
}]) => {

  /* Optional vault name */
  const vault = "";

  /* Optional folder name such as "Clippings/" */

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"""
===================================
08. Record extracellular potentials
===================================

The main output of HNN simulations is the 'dipole' waveform, i.e., the net
intracellular current flowing in pyramidal cell apical dendrites. At the large
distances between cells and M/EEG sensors, this 'primary' current is the main
contributor to the measured fields. Close to the cells, the local field
potential (LFP) is the result of intracellular current leaking into the

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# %%
# %matplotlib widget
import matplotlib.pyplot as plt
from matplotlib.colors import SymLogNorm
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np

from neuron import h
from hnn_core.network_builder import load_custom_mechanisms
from hnn_core.lfp import _LFPElectrode

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# %%
# %matplotlib widget
import matplotlib.pyplot as plt
from matplotlib.colors import SymLogNorm
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np

from neuron import h
from hnn_core.network_builder import load_custom_mechanisms
from hnn_core.lfp import _LFPElectrode

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import os.path as op
import tempfile

import hnn_core
from hnn_core import simulate_dipole, read_params, Network

hnn_core_root = op.dirname(hnn_core.__file__)

params_fname = op.join(hnn_core_root, 'param', 'default.json')
params = read_params(params_fname)

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import os.path as op
import hnn_core
from hnn_core import read_params, Network
import matplotlib.pyplot as plt
from hnn_core.network_builder import NetworkBuilder
from hnn_core.network_builder import _simulate_single_trial
from neuron import h
import numpy as np
from scipy.optimize import curve_fit

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import numpy as np
import matplotlib.pyplot as plt

TSTOP = 5000.
recalc = True

m = 3.4770508e-3
b = -51.231085

# these values were fit over the range [750., 5000]

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# NB on OS X, see psychopy/psychopy PR #1325 and Issue #1315
# To run psychopyApp.py, we need a framework python:
# python.app `which psychopyApp.py`
name: psychopy
channels: !!python/tuple
- defaults
- conda-forge
dependencies:
- CogSci::pygame=1.9.2a0=py27_0
- alabaster=0.7.9=py27_0

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from os import path as op
import numpy as np
# from scipy import linalg

import mne

data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_ave = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')