.. _sphx_glr_auto_examples_plot_neuropop_simul_example.py:


================
Neuropop Example
================

A demonstration of Neuropop using simulated data




.. code-block:: python


    import numpy as np
    import matplotlib.pyplot as plt

    from spykes.ml.neuropop import NeuroPop
    from spykes.utils import train_test_split







Create a NeuroPop object
-----------------------------



.. code-block:: python


    n_neurons = 10
    pop = NeuroPop(n_neurons=n_neurons, tunemodel='glm')







Simulate a population of neurons
-----------------------------



.. code-block:: python


    n_samples = 1000
    x, Y, mu, k0, k, g, b = pop.simulate(pop.tunemodel, n_samples=n_samples,
                                         winsize=400.0)







Split into training and testing sets
-----------------------------



.. code-block:: python


    np.random.seed(42)
    (Y_train, Y_test), (x_train, x_test) = train_test_split(Y, x, percent=0.5)







Fit the tuning curves with gradient descent
-----------------------------



.. code-block:: python


    pop.fit(x_train, Y_train)







Predict the population activity with the fit tuning curves
-----------------------------



.. code-block:: python


    Yhat_test = pop.predict(x_test)







Score the prediction
-----------------------------



.. code-block:: python


    Ynull = np.mean(Y_train, axis=0)
    pseudo_R2 = pop.score(Y_test, Yhat_test, Ynull, method='pseudo_R2')
    print(pseudo_R2)





.. rst-class:: sphx-glr-script-out

 Out::

    [0.012898436115752809, 0.87906982333904071, 0.64953551355040173, 0.71023596752501739, 0.8625152869878786, 0.19022506746859158, 0.87195203613890515, 0.52223737867131592, 0.87889200248158028, 0.89802770436521395]


Plot the simulated and fit tuning curves
-----------------------------



.. code-block:: python


    plt.figure(figsize=[15, 15])

    for neuron in range(pop.n_neurons):
        plt.subplot(4, 3, neuron + 1)
        pop.display(x_test, Y_test[:, neuron], neuron=neuron,
                    ylim=[0.8 * np.min(Y_test[:, neuron]), 1.2 *
                    np.max(Y_test[:, neuron])])

    plt.show()




.. image:: /auto_examples/images/sphx_glr_plot_neuropop_simul_example_001.png
    :align: center




Decode feature from the population activity
-----------------------------



.. code-block:: python


    xhat_test = pop.decode(Y_test)







Visualize ground truth vs. decoded estimates
-----------------------------



.. code-block:: python


    plt.figure(figsize=[6, 5])

    plt.plot(x_test, xhat_test, 'k.', alpha=0.5)
    plt.xlim([-1.2 * np.pi, 1.2 * np.pi])
    plt.ylim([-1.2 * np.pi, 1.2 * np.pi])
    plt.xlabel('Ground truth [radians]')
    plt.ylabel('Decoded [radians]')
    plt.tick_params(axis='y', right='off')
    plt.tick_params(axis='x', top='off')
    ax = plt.gca()
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

    plt.figure(figsize=[15, 5])
    jitter = 0.2 * np.random.rand(x_test.shape[0])
    plt.subplot(132, polar=True)
    plt.plot(x_test, np.ones(x_test.shape[0]) + jitter, 'ko', alpha=0.5)
    plt.title('Ground truth')

    plt.subplot(133, polar=True)
    plt.plot(xhat_test, np.ones(xhat_test.shape[0]) + jitter, 'co', alpha=0.5)
    plt.title('Decoded')
    plt.show()




.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/images/sphx_glr_plot_neuropop_simul_example_002.png
            :scale: 47

    *

      .. image:: /auto_examples/images/sphx_glr_plot_neuropop_simul_example_003.png
            :scale: 47




Score decoding performance
-----------------------------



.. code-block:: python


    # Circular correlation
    circ_corr = pop.score(x_test, xhat_test, method='circ_corr')
    print('Circular correlation: %f' % (circ_corr))





.. rst-class:: sphx-glr-script-out

 Out::

    Circular correlation: 0.743451



.. code-block:: python


    # Cosine distance
    cosine_dist = pop.score(x_test, xhat_test, method='cosine_dist')
    print('Cosine distance: %f' % (cosine_dist))




.. rst-class:: sphx-glr-script-out

 Out::

    Cosine distance: 0.797382


**Total running time of the script:** ( 0 minutes  22.722 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_neuropop_simul_example.py <plot_neuropop_simul_example.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_neuropop_simul_example.ipynb <plot_neuropop_simul_example.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_