Use of eFEL on the models downloaded from the Neocortical Microcircuit Portal
=============================================================================

Requirements: - Python 3.9+, including Pip (https://pip.readthedocs.org)
- A version of Neuron (with Python support) installed on your computer
(for instruction, see https://bbp.epfl.ch/nmc-portal/tools)

Make matplotlib plots show up in the notebook:

.. code:: ipython3

    %matplotlib inline

Install the eFeature Extraction Library:

.. code:: ipython3

    !pip install efel
    import efel

Get a model package from the website

.. code:: ipython3

    !curl -o L5_TTPC2.zip https://bbp.epfl.ch/nmc-portal/assets/documents/static/downloads-zip/L5_TTPC2_cADpyr232_1.zip

Unzip the model package:

.. code:: ipython3

    !unzip L5_TTPC2.zip

Change directory to the model package directory:

.. code:: ipython3

    import os
    os.chdir('L5_TTPC2_cADpyr232_1')

Compile the Neuron mechanisms (if this fails, you might not have
installed Neuron correctly)

.. code:: ipython3

    !nrnivmodl ./mechanisms

Import the model package in Python, and run it:

.. code:: ipython3

    import run
    run.main(plot_traces=True)


.. parsed-literal::

    Warning: no DISPLAY environment variable.
    --No graphics will be displayed.


.. parsed-literal::

    Loading constants
    Setting temperature to 34.000000 C
    Setting simulation time step to 0.025000 ms
    	1 
    	1 
    	1 
    Loading cell cADpyr232_L5_TTPC2_8052133265
    Attaching stimulus electrodes
    Setting up step current clamp: amp=0.593063 nA, delay=700.000000 ms, duration=2000.000000 ms
    Setting up hypamp current clamp: amp=-0.286011 nA, delay=0.000000 ms, duration=3000.000000 ms
    Attaching recording electrodes
    Setting simulation time to 3s for the step currents
    Disabling variable timestep integration
    Running for 3000.000000 ms
    Soma voltage for step 1 saved to: python_recordings/soma_voltage_step1.dat
    Loading cell cADpyr232_L5_TTPC2_8052133265
    Attaching stimulus electrodes
    Setting up step current clamp: amp=0.642485 nA, delay=700.000000 ms, duration=2000.000000 ms
    Setting up hypamp current clamp: amp=-0.286011 nA, delay=0.000000 ms, duration=3000.000000 ms
    Attaching recording electrodes
    Setting simulation time to 3s for the step currents
    Disabling variable timestep integration
    Running for 3000.000000 ms
    Soma voltage for step 2 saved to: python_recordings/soma_voltage_step2.dat
    Loading cell cADpyr232_L5_TTPC2_8052133265
    Attaching stimulus electrodes
    Setting up step current clamp: amp=0.691907 nA, delay=700.000000 ms, duration=2000.000000 ms
    Setting up hypamp current clamp: amp=-0.286011 nA, delay=0.000000 ms, duration=3000.000000 ms
    Attaching recording electrodes
    Setting simulation time to 3s for the step currents
    Disabling variable timestep integration
    Running for 3000.000000 ms
    Soma voltage for step 3 saved to: python_recordings/soma_voltage_step3.dat



.. image:: L5TTPC2_files/L5TTPC2_14_2.png



.. image:: L5TTPC2_files/L5TTPC2_14_3.png



.. image:: L5TTPC2_files/L5TTPC2_14_4.png


Load the output of the model package in numpy array

.. code:: ipython3

    import numpy
    times = []
    voltages = []
    for step_number in range(1,4):
        data = numpy.loadtxt('python_recordings/soma_voltage_step%d.dat' % step_number)
        times.append(data[:, 0])
        voltages.append(data[:, 1])

Prepare the traces for the eFEL

.. code:: ipython3

    traces = []
    for step_number in range(3):
        trace = {}
        trace['T'] = times[step_number]
        trace['V'] = voltages[step_number]
        trace['stim_start'] = [700]
        trace['stim_end'] = [2700]
        traces.append(trace)

Run the eFEL on the trace

.. code:: ipython3

    feature_values = efel.get_feature_values(traces, ['mean_frequency', 'adaptation_index2', 'ISI_CV', 'doublet_ISI', 'time_to_first_spike', 'AP_height', 'AHP_depth_abs', 'AHP_depth_abs_slow', 'AHP_slow_time', 'AP_width', 'peak_time', 'AHP_time_from_peak'])

Plot frequencies over steps

.. code:: ipython3

    import pylab
    for step_number in range(3):
        pylab.bar(step_number, feature_values[step_number]['mean_frequency'][0], align='center')
    pylab.ylabel('Mean frequency (Hz)')
    pylab.xlabel('Step number')
    pylab.xticks(range(3), range(1,4))
    pylab.show()



.. image:: L5TTPC2_files/L5TTPC2_22_0.png


Plot AP height and AHP depth

.. code:: ipython3

    for step_number in range(3):
        time = times[step_number]
        voltage = voltages[step_number]
        peak_times = feature_values[step_number]['peak_time']
        ahp_time = feature_values[step_number]['AHP_time_from_peak']
        ap_heights = feature_values[step_number]['AP_height']
        AHP_depth_abss = feature_values[step_number]['AHP_depth_abs']
        
        pylab.plot(time,voltage)
        pylab.plot(peak_times, ap_heights, 'o')
        pylab.plot(peak_times+ahp_time, AHP_depth_abss, 'o')
        pylab.xlabel('Time (ms)')
        pylab.ylabel('Vm (mV)')
        pylab.show()



.. image:: L5TTPC2_files/L5TTPC2_24_0.png



.. image:: L5TTPC2_files/L5TTPC2_24_1.png



.. image:: L5TTPC2_files/L5TTPC2_24_2.png