HypercubeHopfield

HypercubeHopfield — modern Hopfield associative memory on a Boolean hypercube. Neurons sit on the vertices of a dim-dimensional cube (N = 2^dim). Patterns are stored explicitly and retrieved by softmax attention over a sparse neighborhood — not collapsed into a Hebbian weight matrix, and not full all-to-all modern-Hopfield attention either.

A topology you don't store. Connectivity is the mask table of the Hamming ball — one XOR per neighbor, no adjacency list, at any size. Each vertex attends only inside that ball; cost scales with connections, not with the full cube.

HypercubeAI ecosystem
_{One geometry. Three libraries. Topology-native intelligence.}

HypercubeESN · HypercubeCNN · HypercubeHopfield

HypercubeHopfield is a pillar of HypercubeAI — a family of tools that treat the Boolean hypercube as a first-class computational medium: dynamical reservoirs (ESN), convolutional learning on the same graph (CNN), and associative memory (Hopfield). Shared vertices, shared XOR neighborhoods, no bolted-on grid.

Installation

pip install hypercube-hopfield

Pre-built wheels for Python 3.10–3.13 on Windows, Linux, and macOS. NumPy is the only runtime dependency.

Build from source (C++23 + CMake): see the Python SDK guide.

Quick Start

import numpy as np
import hypercube_hopfield as hh

# 256 neurons (dim=8, N=2^8)
net = hh.HopfieldNetwork(dim=8, seed=42)

patterns = np.random.randn(10, net.num_vertices).astype(np.float32)
net.store_patterns(patterns)

cue = patterns[0] + np.random.randn(net.num_vertices).astype(np.float32) * 0.3
result = net.recall(cue)
print(f"Converged: {result.converged}, steps: {result.steps}")
# result.state is the cleaned recall; input cue is not modified

Features

Explicit pattern storage — modern Hopfield retrieval without a Hebbian weight matrix
Sparse Hamming-ball attention — reach and neighbor_fraction control connectivity and cost
Two update modes — Sync (default, deterministic) and Async (guaranteed energy descent)
Pickle / save / load — persist config and all stored patterns
NumPy integration — automatic float32 conversion; recall does not mutate the cue

Documentation

Python SDK Reference — full API, persistence, errors
Project README — architecture framing, C++ quick start, build
HopfieldNetwork architecture — connectivity, energy, parameters

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hypercube-hopfield 1.0.1

HypercubeHopfield

Installation

Quick Start

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