Cargando…

Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation

HIGHLIGHTS: What are the main findings? A cortical coding method is developed inspired by the network formation in the brain, where the information entropy is maximized while dissipated energy is minimized. The execution time in the cortical coding model is far superior, seconds versus minutes or ho...

Descripción completa

Detalles Bibliográficos
Autores principales: Yucel, Meric, Bagis, Serdar, Sertbas, Ahmet, Sarikaya, Mehmet, Ustundag, Burak Berk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689639/
https://www.ncbi.nlm.nih.gov/pubmed/36421533
http://dx.doi.org/10.3390/e24111678
_version_ 1784836586036264960
author Yucel, Meric
Bagis, Serdar
Sertbas, Ahmet
Sarikaya, Mehmet
Ustundag, Burak Berk
author_facet Yucel, Meric
Bagis, Serdar
Sertbas, Ahmet
Sarikaya, Mehmet
Ustundag, Burak Berk
author_sort Yucel, Meric
collection PubMed
description HIGHLIGHTS: What are the main findings? A cortical coding method is developed inspired by the network formation in the brain, where the information entropy is maximized while dissipated energy is minimized. The execution time in the cortical coding model is far superior, seconds versus minutes or hours, compared to those of the frequently used current algorithms, while retaining comparable distortion rate. What is the implication of the main finding? The significant attribute of the methodology is its generalization performance, i.e., learning rather than memorizing data. Although only vector quantization property was demonstrated herein, the cortical coding algorithm has a real potential in a wide variety of real-time machine learning implementations such as , temporal clustering, data compression, audio and video encoding, anomaly detection, and recognition, to list a few. ABSTRACT: A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy–entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced—seconds versus hours—encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare.
format Online
Article
Text
id pubmed-9689639
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-96896392022-11-25 Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation Yucel, Meric Bagis, Serdar Sertbas, Ahmet Sarikaya, Mehmet Ustundag, Burak Berk Entropy (Basel) Article HIGHLIGHTS: What are the main findings? A cortical coding method is developed inspired by the network formation in the brain, where the information entropy is maximized while dissipated energy is minimized. The execution time in the cortical coding model is far superior, seconds versus minutes or hours, compared to those of the frequently used current algorithms, while retaining comparable distortion rate. What is the implication of the main finding? The significant attribute of the methodology is its generalization performance, i.e., learning rather than memorizing data. Although only vector quantization property was demonstrated herein, the cortical coding algorithm has a real potential in a wide variety of real-time machine learning implementations such as , temporal clustering, data compression, audio and video encoding, anomaly detection, and recognition, to list a few. ABSTRACT: A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy–entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced—seconds versus hours—encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare. MDPI 2022-11-17 /pmc/articles/PMC9689639/ /pubmed/36421533 http://dx.doi.org/10.3390/e24111678 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yucel, Meric
Bagis, Serdar
Sertbas, Ahmet
Sarikaya, Mehmet
Ustundag, Burak Berk
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation
title Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation
title_full Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation
title_fullStr Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation
title_full_unstemmed Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation
title_short Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation
title_sort brain inspired cortical coding method for fast clustering and codebook generation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689639/
https://www.ncbi.nlm.nih.gov/pubmed/36421533
http://dx.doi.org/10.3390/e24111678
work_keys_str_mv AT yucelmeric braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration
AT bagisserdar braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration
AT sertbasahmet braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration
AT sarikayamehmet braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration
AT ustundagburakberk braininspiredcorticalcodingmethodforfastclusteringandcodebookgeneration