arXiv subjects

Hiroshi Inazawa

Publications and source records attributed to Hiroshi Inazawa.

5 recordsLinked to original sources

arXiv2026

Associative Memory using Attribute-Specific Neuron Groups-2: Learning and Sequential Associative Recall between Cue Neurons for different Cue Balls

This paper introduces a neural network model that learns multiple attributes as images and performs associated, sequential recall of the learned memories. Briefly, the model presented here is an associative memory model that extends previous models [1] by increasing the number of attributes. In the real world, memory recall generates a chain of associations consisting of complex and diverse data with meaningful relations. However, because this experimental system is designed to implement and verify the processing operations behind such operations, we believe it is not a problem if the associative memory (i.e., the chain of data) is composed of attributes that do not necessarily have clear relation with each other. Accordingly, the attribute-processing systems prepared in this study consist of five types: the C.CB-RN system for processing color attributes, the S.CB-RN system for shape attributes, and the V.CB-RN system for size attributes, as adopted in our previous paper [1], as well as the SV.CB-RN system for processing the names of the world's most beautiful scenery (spectacular view names) and the CN.CB-RN system for processing constellation names. As before, the data presented to each CB-RN system are represented as image patterns using QR codes [2]. These five types of CB-RN systems will be combined and trained with QR code pattern images of the attribute elements of each system. After that, when a pattern image of an attribute element is presented to any of the CB-RN systems, a mechanism will be constructed in which a chain (associative) recall of pattern images of related attribute elements in the other trained systems will be generated.

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arXiv2025

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

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].

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arXiv2025

Associative Memory Model with Neural Networks: Memorizing multiple images with one neuron

This paper presents a neural network model (associative memory model) for memory and recall of images. In this model, only a single neuron can memorize multi-images and when that neuron is activated, it is possible to recall all the memorized images at the same time. The system is composed of a single cluster of numerous neurons, referred to as the "Cue Ball," and multiple neural network layers, collectively called the "Recall Net." One of the features of this model is that several different images are stored simultaneously in one neuron, and by presenting one of the images stored in that neuron, all stored images are recalled. Furthermore, this model allows for complete recall of an image even when an incomplete image is presented

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arXiv2025

The Method for Storing Patterns in Neural Networks-Memorization and Recall of QR code Patterns-

In this paper, we propose a mechanism for storing complex patterns within a neural network and subsequently recalling them. This model is based on our work published in 2018(Inazawa, 2018), which we have refined and extended in this work. With the recent advancements in deep learning and large language model (LLM)-based AI technologies (generative AI), it can be considered that methodologies for the learning are becoming increasingly well-established. In the future, we expect to see further research on memory using models based on Transformers (Vaswani, et. al., 2017, Rae, et. al., 2020), but in this paper we propose a simpler and more powerful model of memory and recall in neural networks. The advantage of storing patterns in a neural network lies in its ability to recall the original pattern even when an incomplete version is presented. The patterns we have produced for use in this study have been QR code (DENSO WAVE, 1994), which has become widely used as an information transmission tool in recent years.

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arXiv2022

An associative memory model with very high memory rate: Image storage by sequential addition learning

In this paper, we present a neural network system related to about memory and recall that consists of one neuron group (the "cue ball") and a one-layer neural net (the "recall net"). This system realizes the bidirectional memorization learning between one cue neuron in the cue ball and the neurons in the recall net. It can memorize many patterns and recall these patterns or those that are similar at any time. Furthermore, the patterns are recalled at most the same time. This model's recall situation seems to resemble human recall of a variety of similar things almost simultaneously when one thing is recalled. It is also possible for additional learning to occur in the system without affecting the patterns memorized in advance. Moreover, the memory rate (the number of memorized patterns / the total number of neurons) is close to 100%; this system's rate is 0.987. Finally, pattern data constraints become an important aspect of this system.

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