# Data Science & Data Skills for Neuroscientists (J.W. Pillow, SfN 2016)

The material proposed in these tutorials revisits what was presented by [Jonathan Pillow](https://pillowlab.princeton.edu) in a "short course" on [Data Science and Data Skills for Neuroscientists](https://neuronline.sfn.org/scientific-research/data-science-and-data-skills-for-neuroscientists) organized at the SFN 2016 meeting, constructing and fitting models with [`pynapple`](https://pynapple.org/) and [`NeMoS`](https://nemos.readthedocs.io/en/latest/index.html).

The original Matlab implementation and its python translation can be found at the following links:

- Matlab: [https://github.com/pillowlab/GLMspiketraintutorial](https://github.com/pillowlab/GLMspiketraintutorial)
- Python:  [https://github.com/pillowlab/GLMspiketraintutorial_python](https://github.com/pillowlab/GLMspiketraintutorial_python)


<h2 style="font-size: 2em; font-weight: bold; margin-top: 20px; margin-bottom: 10px;">Contents</h2>


```{toctree}
:maxdepth: 2

01_poisson_glm.md
```

```{toctree}
:maxdepth: 2

02_spike_history_coupled_glm.md
```

```{toctree}
:maxdepth: 2

03_04_regularization.md
```

```{toctree}
:maxdepth: 2

05_decoding.md
```

## What's changed

- Tutorial 3 and 4 on regularization have been merged into a single one.
- In the regularization tutorial, an example is shown on how to smooth over multiple predictors.

## Citation

If you found this material useful and you wish to cite this tutorial, feel free to :
- Acknowledge the paper from which it was developed: [Pillow et al, *Nature* 2008](https://pillowlab.princeton.edu/pubs/abs_Pillow08_nature.html)
- Acknowlege `pynapple` package by citing the [accompanying paper](https://pynapple.org/citing.html). 
- Acknowledge `NeMoS` package by citing the [associated DOI](https://nemos.readthedocs.io/en/latest/citation.html).