[Paper] Associative Memory using Attribute-Specific Neuron Groups-1: Learning between Multiple Cue Balls

Source: arXiv - 2512.02319v1

Overview

Hiroshi Inazawa introduces a fresh take on associative memory by wiring together attribute‑specific neuron groups—one for color, one for shape, and one for size. Building on the earlier Cue‑Ball/Recall‑Net (CB‑RN) framework, the paper shows how a network can store and retrieve multiple visual cues simultaneously, using simple 2‑D QR‑code encodings as stand‑ins for real images.

Key Contributions

• Attribute‑specific CB‑RN modules (C‑CB‑RN, S‑CB‑RN, V‑CB‑RN) that process color, shape, and size independently yet cooperate during recall.
• Unified 2‑D QR‑code representation for each visual attribute, enabling a compact, hardware‑friendly encoding of image features.
• Demonstration of multi‑cue associative recall, where presenting any subset of attributes triggers the reconstruction of the full image pattern.
• Scalable architecture that can be extended to additional attributes (e.g., texture, orientation) without redesigning the whole network.
• Empirical evaluation of recall accuracy and robustness against noisy or missing cues.

Methodology

1. Cue Balls & Recall Net – Each “Cue Ball” is a small, fully‑connected layer that receives a binary QR‑code representing a single attribute (e.g., a 32×32 QR pattern for color). The three Cue Balls feed into a shared Recall Net that learns to associate the three attribute vectors with a target output (the composite image code).
2. Training – The system is trained with pairs {(color‑QR, shape‑QR, size‑QR) → composite‑QR}. Standard back‑propagation updates the weights of both Cue Balls and the Recall Net.
3. Testing / Retrieval – During recall, any combination of the three QR inputs (including a single cue) is presented. The network’s output is decoded back into the full composite QR, which can be visualized as the original image.
4. Evaluation Metrics – Recall quality is measured by pixel‑wise Hamming distance between the generated QR and the ground‑truth composite QR, as well as by classification accuracy when the recovered QR is fed to a downstream image recognizer.

The approach stays deliberately simple: binary QR codes act as a plug‑and‑play interface that can be generated on‑the‑fly, making the model easy to prototype on CPUs, GPUs, or even micro‑controllers.

Results & Findings

Key take‑aways

• Attribute independence does not hinder joint recall; the network learns cross‑attribute regularities.
• Graceful degradation: performance declines smoothly as cues are removed or corrupted, a desirable property for real‑world systems where sensor data may be incomplete.

Practical Implications

• Content‑Based Image Retrieval – Store images as a set of attribute QR codes; a user can query with just color or shape and still retrieve the full item.
• Robotics & Vision – A robot equipped with cheap color, shape, and size sensors can reconstruct a richer scene representation without needing a full camera feed.
• Edge AI – QR‑code vectors are tiny (a few hundred bits), enabling associative memory on low‑power devices (e.g., IoT gateways) that cannot run heavyweight CNNs.
• Memory‑augmented Applications – The model can serve as a lightweight “scratch‑pad” for systems that need fast associative lookup (e.g., recommendation engines that match on partial user preferences).
• Explainability – Because each attribute is processed by a dedicated neuron group, developers can inspect which cue contributed most to a recall, aiding debugging and model transparency.

Limitations & Future Work

• Scalability of QR size – Larger images require bigger QR codes, which quickly increase the dimensionality of the Cue Balls and may strain memory on embedded hardware.
• Fixed attribute set – The current design assumes three pre‑defined attributes; adding new ones requires training a fresh Cue Ball module.
• Synthetic data bias – Experiments rely on artificially generated QR codes rather than raw pixel images, so real‑world performance on natural photographs remains to be validated.
• Future directions suggested by the author include:
  1. Integrating continuous‑valued feature encoders (e.g., learned embeddings) instead of binary QR codes.
  2. Exploring hierarchical cue structures for more complex scenes.
  3. Benchmarking against modern associative memory models such as Hopfield networks with attention mechanisms.

Authors

• Hiroshi Inazawa

Paper Information

• arXiv ID: 2512.02319v1
• Categories: cs.NE
• Published: December 2, 2025
• PDF: Download PDF