Blazingly fast cognitive complexity analysis for Python, written in Rust.

A foundational library for Semantic Hypergraphs

NGC-Learn: Computational Neuroscience and NeuroAI in Python

Python package for extracting representations from state-of-the-art computer vision models

A probabilistic programming language for metacognitive modeling

Platform-independent lightweight Python library for designing and conducting timing-critical behavioural and neuroimaging experiments

CVPR 2022: Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?

Modern port of Melanie Mitchell's and Douglas Hofstadter's Copycat

A block modeling system for cognitive neuroscience

Deep active inference agents using Monte-Carlo methods

Persistent memory for Claude Code built on computational neuroscience, not just retrieval. 36 cited brain mechanisms consolidate what matters, keep it current as your project evolves, and reconstruct the right context at the right time — a living memory, not a flat RAG. Says "I don't know" when unsure, flags contradictions. Local-first · MCP · MIT.

Unleashing the Power of Cognitive Dynamics on Large Language Models

Implementation/simulation of the predictive forward-forward credit assignment algorithm for training neurobiologically-plausible recurrent neural network models.

A sovereign cognitive architecture with IIT 4.0 integrated information, residual-stream affective steering (CAA), Global Workspace Theory, active inference, and 72 consciousness modules — running locally on Apple Silicon.

Battery of standard cognitive psychology tasks

A framework bridging cognitive science and LLM reasoning research to diagnose and improve how large language models reason, based on analysis of 192K model traces and 54 human think-aloud traces.

Analysis scripts for NNDb-3T+

Implementation/simulation of active neural generative coding (ANGC) for training neurobiologically-plausible active inference agent models.

Implementation of the Semantic Pointer Architecture for Nengo

Code for "Learning Inductive Biases with Simple Neural Networks" (Feinman & Lake, 2018).