Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation

Recent achievements on CNN (convolutional neural networks) and DNN (deep neural networks) researches provide a lot of practical applications on computer vision area. However, these approaches require construction of huge size of training data for learning process. This paper tries to find a way for...

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Autores principales:	Kyuchang Kang, Changseok Bae
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	visual stimuli sparse distributed representation morphological semantic recall Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/7f53c8f0b66345fdbda50c48f3ba2abe
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spelling	oai:doaj.org-article:7f53c8f0b66345fdbda50c48f3ba2abe2021-11-25T16:38:05ZMemory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation10.3390/app1122107862076-3417https://doaj.org/article/7f53c8f0b66345fdbda50c48f3ba2abe2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10786https://doaj.org/toc/2076-3417Recent achievements on CNN (convolutional neural networks) and DNN (deep neural networks) researches provide a lot of practical applications on computer vision area. However, these approaches require construction of huge size of training data for learning process. This paper tries to find a way for continual learning which does not require prior high-cost training data construction by imitating a biological memory model. We employ SDR (sparse distributed representation) for information processing and semantic memory model, which is known as a representation model of firing patterns on neurons in neocortex area. This paper proposes a novel memory model to reflect remembrance of morphological semantics of visual input stimuli. The proposed memory model considers both memory process and recall process separately. First, memory process converts input visual stimuli to sparse distributed representation, and in this process, morphological semantic of input visual stimuli can be preserved. Next, recall process can be considered by comparing sparse distributed representation of new input visual stimulus and remembered sparse distributed representations. Superposition of sparse distributed representation is used to measure similarities. Experimental results using 10,000 images in MNIST (Modified National Institute of Standards and Technology) and Fashion-MNIST data sets show that the sparse distributed representation of the proposed model efficiently keeps morphological semantic of the input visual stimuli.Kyuchang KangChangseok BaeMDPI AGarticlevisual stimulisparse distributed representationmorphological semanticrecallTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10786, p 10786 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	visual stimuli sparse distributed representation morphological semantic recall Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	visual stimuli sparse distributed representation morphological semantic recall Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Kyuchang Kang Changseok Bae Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
description	Recent achievements on CNN (convolutional neural networks) and DNN (deep neural networks) researches provide a lot of practical applications on computer vision area. However, these approaches require construction of huge size of training data for learning process. This paper tries to find a way for continual learning which does not require prior high-cost training data construction by imitating a biological memory model. We employ SDR (sparse distributed representation) for information processing and semantic memory model, which is known as a representation model of firing patterns on neurons in neocortex area. This paper proposes a novel memory model to reflect remembrance of morphological semantics of visual input stimuli. The proposed memory model considers both memory process and recall process separately. First, memory process converts input visual stimuli to sparse distributed representation, and in this process, morphological semantic of input visual stimuli can be preserved. Next, recall process can be considered by comparing sparse distributed representation of new input visual stimulus and remembered sparse distributed representations. Superposition of sparse distributed representation is used to measure similarities. Experimental results using 10,000 images in MNIST (Modified National Institute of Standards and Technology) and Fashion-MNIST data sets show that the sparse distributed representation of the proposed model efficiently keeps morphological semantic of the input visual stimuli.
format	article
author	Kyuchang Kang Changseok Bae
author_facet	Kyuchang Kang Changseok Bae
author_sort	Kyuchang Kang
title	Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
title_short	Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
title_full	Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
title_fullStr	Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
title_full_unstemmed	Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
title_sort	memory model for morphological semantics of visual stimuli using sparse distributed representation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7f53c8f0b66345fdbda50c48f3ba2abe
work_keys_str_mv	AT kyuchangkang memorymodelformorphologicalsemanticsofvisualstimuliusingsparsedistributedrepresentation AT changseokbae memorymodelformorphologicalsemanticsofvisualstimuliusingsparsedistributedrepresentation
_version_	1718413086690377728

Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation

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