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 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.