Bioinspired Cortex-based Fast Codebook Generation

Yücel, Meriç; Bagis, Serdar; Sertbaş, Ahmet; Sarıkaya, Mehmet Zeki; Üstündağ, Burak Berk

doi:10.48550/arxiv.2201.12322

Cited by 1 publication

(3 citation statements)

References 37 publications

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“…The spine nodes are the candidates that evolve to become cortex nodes, i.e., a well-trained spine node evolves into a cortex node. The detailed explanation of cortical coding network and its performance can be found in [11] and [37]. There are also other differences between cortex and spine nodes that define their structural characteristics and functions.…”

Section: Methodsmentioning

confidence: 99%

“…The cortical coding method introduced here addresses these difficulties and overcomes the limitations in anomaly detection in time series data in a wide variety of cases, as we demonstrate by using commonly employed data set. The major characteristics of the cortical coding method [37] and the contributions of the work can be summarized as follows. The method:…”

Section: Accurate and Early Detection Of Anomalies In Time Series Dat...mentioning

confidence: 99%

“…Increasing the entropy at each level also increases the overall entropy at the leaves of the cortical coding tree. More detailed mathematical background of the entropy maximization in cortical coding network can be found in [11] and [37].…”

Section: A Training and Development Of The Cortical Networkmentioning

confidence: 99%

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High Performance Time Series Anomaly Detection Using Brain Inspired Cortical Coding Method

2023

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Accurate and automated anomaly detection in time series data sets has an increasingly important role in a wide range of applications. Inspired by coding in the cortical networks of the brain, here we introduce a novel approach for high performance real-time anomaly detection. Cortical coding method is adaptive and dynamic, consisting of self-organized networks. In the cortical coding network introduced herein, the morphological structuring is driven by a brain inspired feature extraction strategy that aims the minimization of the signal energy dissipation while increasing the information entropy of the system. We combine the cortical coding network with transform coding and multi resolution analysis for anomaly detection. As we demonstrate here, the new coding methodology provides high computational efficiency in addition to scalability with respect to target accuracy compared to the traditional clustering algorithms. A wide variety of data sets are used to demonstrate time series anomaly detection performance. In a preliminary work presented here, we detected 77.6% of the present anomalies correctly, using the same hyperparameters for every stage of the method. The results are compared with several clustering algorithms such as K-means and its variants mini-batch K-means, sequential K-means and finally with hierarchical agglomerative clustering. Additionally, the performance of all the clustering methods are compared by memorizing all input data set without performing any clustering. The cortical coding method has shown the best performance compared to the other methods. From the results achieved so far, it appears that there is still a significant room for improvement of the success rate by, specifically, performing hyperparameter and filter optimization according to the characteristics of data sets and using a more advanced fusion model at the output layer. Low time and space complexity, high generalization performance, suitability to real-time anomaly detection, and in-memory processing compatibility are distinct advantages of the cortical coding method in a variety of anomaly detection problems, such as predictive maintenance, cybersecurity, telemedicine, risk management, and transportation safety.

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Section: Methodsmentioning

confidence: 99%

Section: Accurate and Early Detection Of Anomalies In Time Series Dat...mentioning

confidence: 99%