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

TL;DR

This paper introduces a neural network model for associative memory that utilizes attribute-specific neuron groups to recall multiple images based on attributes like color, shape, and size, using a system of specialized Cue Balls and Recall Nets.

Contribution

It proposes a novel neural network architecture that employs attribute-specific representations for associative memory, extending previous models to handle multiple attributes and images.

Findings

Successfully recalled multiple images based on attribute cues

Utilized QR codes for image pattern representation

Demonstrated effective attribute-based recall in the model

Abstract

In this paper, we present a new neural network model based on attribute-specific representations (e.g., color, shape, size), a classic example of associative memory. The proposed model is based on a previous study on memory and recall of multiple images using the Cue Ball and Recall Net (referred to as the CB-RN system, or simply CB-RN) [1]. The system consists of three components, which are C.CB-RN for processing color, S.CB-RN for processing shape, and V.CB-RN for processing size. When an attribute data pattern is presented to the CB-RN system, the corresponding attribute pattern of the cue neurons within the Cue Balls is associatively recalled in the Recall Net. Each image pattern presented to these CB-RN systems is represented using a two-dimensional code, specifically a QR code [2].