Despite decades of research, scientists have yet to create an artificial neural network capable of rivaling the speed and accuracy of the human visual cortex. Now, Haizhou Li and Huajin Tang at the A*STAR Institute for Infocomm Research and co-workers in Singapore propose using a spiking neural network (SNN) to solve real-world pattern recognition problems. Artificial neural networks capable of such pattern recognition could have broad applications in biometrics, data mining and image analysis.
Humans are remarkably good at deciphering handwritten text and spotting familiar faces in a crowd. This ability stems from the visual cortex—a dedicated area at the rear of the brain that is used to recognize patterns, such as letters, numbers and facial features. This area contains a complex network of neurons that work in parallel to encode visual information, learn spatiotemporal patterns and classify objects based on prior knowledge or statistical information extracted from patterns.
Read more at: Phys.org