Reference implementation of "An Algorithm for Routing Vectors in Sequences" (Heinsen, 2022) and "An Algorithm for Routing Capsules in All Domains" (Heinsen, 2019), for composing deep neural networks.

Implementation of deep implicit attention in PyTorch

Holistic system for storage and transformation of information based on associative model of data. Целостная система для хранения и обработки информации, основанная на ассоциативной модели данных.

Physics-inspired transformer modules based on mean-field dynamics of vector-spin models in JAX

Cognitive Computing with Associative Memory

This repository contains the official code for Energy Transformer---an efficient Energy-based Transformer variant for graph classification

Implementation of Hopfield Neural Network in Python based on Hebbian Learning Algorithm

[ICML 2024] Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models

Recurrent predictive coding networks for associative memory employing covariance learning

PyTorch implementation of IJCAI 2020 paper Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network

Minimum Description Length Hopfield Networks

Open Source C++ Library for Pseudo-inverse Fully Connected Recurrent Neural Networks (from my PhD)

Source-codes and examples of quaternion-valued recurrent projection neural networks on unit quaternions

Secure your data with personal memories, not passwords

Simulation code for simulations run in my PhD

Publications by Peter Overmann

A neuro-inspired computational framework for modeling memory consolidation in neural networks, with applications from toy models to human connectomics.

Metacognitive AI architecture combining Modern Hopfield Networks with entropy-based confusion detection for insight-like problem solving. Implements energy-based search, adaptive beta scaling, and a Cognitive Database integration for analogical reasoning and topology tunneling.

This Repository contains scratch implementation of Soft Computing Lab practicals

hclust_mix is a Python script that allows the identification of attractor states from gene expression matrices using Hopfield neural networks.