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A Python Toolbox for Multimode Neural Data Representation Analysis - A Representational Analysis Toolbox for Neuroscience, including Neural Pattern Similarity (NPS), Representational Similarity Analysis (RSA), Spatiotemporal Pattern Similarity (STPS) & Inter-Subject Correlation (ISC)

Home Page: https://zitonglu1996.github.io/NeuroRA/

License: MIT License

Python 99.99% Jupyter Notebook 0.01%
python-toolbox representation-similarity rsa neural-data eeg meg fmri ecog seeg fnirs

neurora's Introduction

#NeuroRA

A Python Toolbox of Representational Analysis from Multimodal Neural Data

Overview

Representational Similarity Analysis (RSA) has become a popular and effective method to measure the representation of multivariable neural activity in different modes.

NeuroRA is an easy-to-use toolbox based on Python, which can do some works about RSA among nearly all kinds of neural data, including behavioral, EEG, MEG, fNIRS, sEEG, ECoG, fMRI and some other neuroelectrophysiological data. In addition, users can do Neural Pattern Similarity (NPS), Spatiotemporal Pattern Similarity (STPS), Inter-Subject Correlation (ISC), Classification-based EEG Decoding and a novel cross-temporal RSA (CTRSA) on NeuroRA.

Installation

pip install neurora

Paper

Lu, Z., & Ku, Y. (2020). NeuroRA: A Python toolbox of representational analysis from multi-modal neural data. Frontiers in Neuroinformatics. 14:563669. doi: 10.3389/fninf.2020.563669

Website & How to use

See more details at the NeuroRA website.

You can read the Documentation here or download the Tutorial here to know how to use NeuroRA.

Required Dependencies:

  • Numpy: a fundamental package for scientific computing.
  • SciPy: a package that provides many user-friendly and efficient numerical routines.
  • Scikit-learn: a Python module for machine learning.
  • Matplotlib: a Python 2D plotting library.
  • NiBabel: a package prividing read +/- write access to some common medical and neuroimaging file formats.
  • Nilearn: a Python module for fast and easy statistical learning on NeuroImaging data.
  • MNE-Python: a Python software for exploring, visualizing, and analyzing human neurophysiological data.

Features

  • Calculate the Neural Pattern Similarity (NPS)

  • Calculate the Spatiotemporal Neural Pattern Similarity (STPS)

  • Calculate the Inter-Subject Correlation (ISC)

  • Calculate the Representational Dissimilarity Matrix (RDM)

  • Calculate the Cross-Temporal RDM (RDM)

  • Calculate the Representational Similarity based on RDMs

  • One-Step Realize Representational Similarity Analysis (RSA)

  • Conduct Cross-Temporal RSA (CTRSA)

  • Conduct Classification-based EEG decoding

  • Conduct Statistical Analysis

  • Save the RSA result as a NIfTI file for fMRI

  • Plot the results

Demos

There are several demos for NeuroRA, and you can see them in /demos/.. path (both .py files and .ipynb files are provided).

Run the Demo View the Demo
Demo 1 Open In Colab View the notebook
Demo 2 Open In Colab View the notebook
Demo 3 Open In Colab View the notebook

About NeuroRA

Noteworthily, this toolbox is currently only a test version. If you have any question, find some bugs or have some useful suggestions while using, you can email me and I will be happy and thankful to know.

My email address: [email protected] / [email protected]

My personal homepage: https://zitonglu1996.github.io

neurora's People

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neurora's Issues

bhvRDM for representations in CNNs

First of all, thank you so much for making this work open source! It is much appreciated! :)

I am interested in calculating RDMs for representations learned by neural networks. For this, I was looking at your bhvRDM function, since the description of the function includes 'This function can also be used to calculate the RDM for computational simulation data'.

My understanding was that for RDMs, correlation distance (i.e. 1 - r where r refers to the Pearson correlation) was used. However, on line 84 (here), I am not sure why you are simply computing the absolute distance. Can you please help me out?

Thanks once again for your phenomenal work!

why not remove .idea directory

.idea directory is the configuration directory of JetBrains IDEs such as Intellij idea or Pycharm. It's your own configuration, so it's helpless to others.

Could this approach be extended to work on high dimensional spike train data?

First of all, thank you so much for making this work open source! It is much appreciated! :)

Could this approach be extended to work on high dimensional spike train data? If so which classes which would be the best classes to extend and inherit from in developing a new spike train based approach?

I think PySpike is doing something similar to representational similarity analysis of spike trains, but I don't know if it is interoperable with approaches here.

Thanks,

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