Using filtered point process modeling to disentangle neuronal contributions to electrophysiological power spectra

Interactive tutorials developed with the learnr package supporting the textbook OpenIntro::Introduction to Modern Statistics.

Code behind the work "Single Cortical Neurons as Deep Artificial Neural Networks", published in Neuron 2021

🎯 Learning objectives for OpenIntro Statistics

👩🏻‍🏫 Slides for OpenIntro Statistics

Python code for "Probabilistic Machine learning" book by Kevin Murphy

NEural MOdelS, a statistical modeling framework for neuroscience.

PyHRF is a set of tools to analyze fMRI data and specifically study hemodynamics.

Code repository of implementing the Common Oscillator Model

State-space Oscillator Modeling And Time-series Analysis (SOMATA) is a Python library for state-space neural signal processing algorithms developed in the Purdon Lab.

This is the repo for Hugo Soulat's Nature Scientific Reports paper on PAC. This algorithm will be later added to SOMATA.

An open access book on scientific visualization using python and matplotlib

Multi-class confusion matrix library in Python

NMA Computational Neuroscience course

Integrative Reduced Rank Regression with Multi-View Predictors

Neuroscience data analysis framework for reproducible research built by Loren Frank Lab at UCSF

A list of openly available datasets in (mostly human) electrophysiology.

Parameterizing neural power spectra into periodic & aperiodic components.