Dense associative memory and modern Hopfield networks
Basic triadic memory implementation.
Fusion system for combining algebraic structures
An associative memory system using spreading activation with SQLite FTS5 full-text search
Reproducing Kernel Hilbert Space: kernels, MMD, and kernel quantile embeddings (re-exports hopfield for AM)
Reconstructive memory retrieval engine using ACT-R spreading activation
Holographic memory systems built on amari-holographic — the optical table for holographic computing
Persistent memory engine for AI coding assistants
CLI entry point for the Codemem memory engine
Shared types, traits, and errors for the Codemem memory engine
Candle-based embedding service for Codemem using BAAI/bge-base-en-v1.5
Graph engine for Codemem with petgraph algorithms and SQLite persistence
This is a ruby gem that lets you implement categorization systems with ease. Associative memory neural networks make it easy to identify probable patterns between sets of named data points. It can be cumbersome to interface with the neural network directly, however, as a typical implementation has a fixed size and training period, which limits how useful they can be to an integrated system. associative_memory simplifies these kind of machine learning models by offering dynamic input and output sets. This allows your code to concentrate on extrapolating meaningful patterns rather than juggling bitmasks and transposition matrices.
A Fuzzy Associative Memory (FAM for short) is a Fuzzy Logic tool for decision making. Fuzzy logic FAMs have a wide range of practical applications: Control systems, such as governing a fan to keep a room at the "just right" temperature; Game AI, such as imbuing bots with human-like decision-making behavior; Prediction systems, linking causes with effects. A FAM uses Fuzzy Sets to establish a set of rules that are linguistic in nature. The linguistic rules, and the fuzzy sets they contain, are defined by a human "expert" (presumably, you). The rules therefore codify intelligence and map this knowledge from the human domain to the digital.