  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 sensory cortical networks in the brain. Dubbed as
bioinspired cortex, the algorithm provides convergence to orthogonal features
from streaming signals with superior computational efficiency while processing
data in 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 cortex 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.
