This project is part of my Master of Sciences graduate studies at Federal University of ABC.

You must have a working python 3 installation. We recommend using anaconda.

We recommend creating a virtual environment to work with the package. If you are using conda, follow the instructions below or learn more at their docs. From within the terminal (for linux) or conda console (for windows), run conda create --name spike python=3 Before installing the package, activate the environment using source activate spike.

git clone https://github.com/EstevaoUyra/spikelearn
cd spikelearn
pip install .

The installation is currently taking some minutes to complete, but the final installation is very light (~160 kb).

Not implemented yet

import spikelearn as spk

from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()

res = spk.shuffle_val_predict(clf, df)
res.confusion_matrix();
res.score

Time perception is discussed since Aristotle, yet its underlying neural basis remains elusive. By studying neural activity measured not only after but also during learning, we hope to make original contributions to the field.

If we had interest only in single variable correlations, like the famous Time Cells or Ramping Neurons, we could get along with traditional analysis. To go further into population coding, we apply machine learning techniques such as Logistic Regression, Support Vector Machines, XGBoost, and I hope to soon use LSTMs.

It is impossible to overstate the Python community contributions to my work, from the libraries (sklearn, pandas, numpy), to the foruns (stack overflow). Even without strong principles, one should feel obliged to contribute back in the possible means.

I strongly believe in the principles of open science, and in the power of collaboration for the greater good, which's the reason why I am learning conventions and best practices for documentation and trying to make it all as organized as possible, even though organization was never my strength.

I intend to integrate my functions with pip in a stable package sometime before ending my Master's (scheduled to end September/2019).

We mostly followed the organization proposed by Cookiecutter Data Science. The specific usage for each of the main folders in this project follows:

Unfortunately our data is not available yet, and is being ignored by GitHub for the moment. We separate it by the level of preprocessing, aka raw, interim and processed. We also have a folder for external data, and one for results, the latter ignored mainly for storage purposes.

All functions, classes and analysis procedures will be stored in the library folder spikelearn. It is divided into data, measures, models, tools and visuals. TODO explain each subfolder

All code that will be run, the scrips, is located in the src directory, where it is divided into analysis, data, EFO, models, and visualization. More information about the src folder on the README inside it.

• Separate shuffle val cross prediction and direct prediction

• Unit testing

• Measure coverage

• Add pip stable and ReadTheDocs

• Start logging