.. _sphx_glr_auto_examples_00_encodingdecoding:

########
Examples
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Some introduction to the encoding/decoding framework.


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we fit a residual noise covariance matrix to simulated data from a VonMisesPRF-model.">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_fit_residuals_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/fit_residuals`

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      <div class="sphx-glr-thumbnail-title">Fit the residual covariance matrix</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="It can be helpful to _constrain_ the decoding of a stimulus. For example, we might no be interested in decoding the location of the stimulus, but, rather, want to know which part of the stimulus show most drive.">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_masked_stimulus_decoding_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/masked_stimulus_decoding`

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      <div class="sphx-glr-thumbnail-title">Stimulus decoding using stimulus mask</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="When it comes to fitting encoding models to fMRI data, the most common approach is to use a linear encoding model, were the predicted BOLD response is a linear combination of different neural populations with predefined tuning properties.">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_linear_encoding_model_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/linear_encoding_model`

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      <div class="sphx-glr-thumbnail-title">Linear encoding model</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we create a Gaussian PRF model and plot the predictions. We also simulate data and then estimate back the generating parameters.">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_encoding_model_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/encoding_model`

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      <div class="sphx-glr-thumbnail-title">Create a simple Gaussian Prf encoding model</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Here we invert different encoding models">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_invert_model_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/invert_model`

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      <div class="sphx-glr-thumbnail-title">Invert encoding model</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Here we will simulate neural data given a ground truth encoding model  and try to decode the stimulus from the data.">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_decode_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/decode`

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      <div class="sphx-glr-thumbnail-title">Decoding of stimuli from neural data</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Recontruct a 2D visual stimulus from real fMRI data">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_decode_v1_thumb.png
    :alt:

  :doc:`/auto_examples/00_encodingdecoding/decode_v1`

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      <div class="sphx-glr-thumbnail-title">Recontruct a 2D visual stimulus from real fMRI data</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Here we fit a 2D PRF model to data from the Szinte (2024)-dataset.">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_decode_visual_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/decode_visual`

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      <div class="sphx-glr-thumbnail-title">Fit a 2D PRF model</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Fisher information is defined as the expected value of the square of the derivative of the log-likelihood with respect to some parameter.">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_fisher_information_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/fisher_information`

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      <div class="sphx-glr-thumbnail-title">Fisher information to estimate precision of encoding parameters across stimulus space</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example script we will try out increasingly complex models for visual population receptive fields (PRFs). We will start with a simple Gaussian PRF model, and then add more complexity step by step.">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_fit_prf_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/fit_prf`

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      <div class="sphx-glr-thumbnail-title">Different flavors of visual population receptive field models</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Here, we are decoding oriented stimuli from neural data that have an extra dimension: amplitude.">

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  .. image:: /auto_examples/00_encodingdecoding/images/thumb/sphx_glr_decode2d_thumb.png
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  :doc:`/auto_examples/00_encodingdecoding/decode2d`

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      <div class="sphx-glr-thumbnail-title">Decoding 2D stimuli from neural data</div>
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    </div>


.. toctree::
   :hidden:

   /auto_examples/00_encodingdecoding/fit_residuals
   /auto_examples/00_encodingdecoding/masked_stimulus_decoding
   /auto_examples/00_encodingdecoding/linear_encoding_model
   /auto_examples/00_encodingdecoding/encoding_model
   /auto_examples/00_encodingdecoding/invert_model
   /auto_examples/00_encodingdecoding/decode
   /auto_examples/00_encodingdecoding/decode_v1
   /auto_examples/00_encodingdecoding/decode_visual
   /auto_examples/00_encodingdecoding/fisher_information
   /auto_examples/00_encodingdecoding/fit_prf
   /auto_examples/00_encodingdecoding/decode2d