A list of schools and researchers in computational neuroscience

PyHGF: A neural network library for predictive coding

The Julia implementation of the generalised hierarchical Gaussian filter

jsPsych tasks made by the Niv Lab

cpm is a fundamental scientific modelling library for Computational Psychiatry.

Tools for serving and storing data from online experiments.

Code associated with "Anxiety, avoidance, and sequential evaluation"

Collection of publicly available cognitive task datasets

Tools for computational psychiatry research.

Digital version of the widely used Trail Making Test using jsPsych library in JavaScript

Code for "Improving the reliability of cognitive task measures: A narrative review".

Code and data for "An item response theory analysis of the Matrix Reasoning Item Bank (MaRs-IB)"

Depression recognition via resting‑state EEG using Phase Lag Index (PLI) functional connectivity and ANOVA F‑test feature selection. Baseline SVM and MLP classifiers are compared, and an enhanced SVM with grid search optimisation achieves 85.85% accuracy, 82.50% sensitivity, and 93.94% AUC on the MODMA dataset.

[In Production] Adaptation of Nathaniel Daw's Two-Step Sequential Learning Task. Designed for a study of reward prediction for food with college undergraduates.

[In production] Adaptation of Nathaniel Daw's Two-Step Sequential Learning Task. Designed for a study of AN patients and healthy controls.

EEG entropy rate: a sub‑critical biomarker for Alzheimer's disease – fully reproducible Python pipeline for open neuroscience.

A collection of experimental paradigms.

machine learning pipeline that analyzes electrical signals from lab-grown mini-brains to see if the brain circuits in schizophrenia and autism models fail to reach the healthy "balanced" state that normal brains settle into.

We provide a semi-automated Python workflow for reproducible BIDS-EEG and computational modelling analysis. It includes preprocessing, optimized ICA, spectral and ERP/IAF analyses, and hierarchical drift-diffusion modelling (HSSM) linking neural, experimental, and clinical measures to latent decision processes, all configured in one JSON file.