DeSTIN: A Scalable Deep Learning Architecture With Application to High-Dimensional Robust Pattern Recognition
The topic of deep learning systems has received significant attention during the past few years, particularly as a biologically-inspired approach to processing high dimensional signals. The latter often involve spatiotemporal information that may span large scales, rendering its representation in the general case highly challenging. Deep learning networks attempt to overcome this challenge by means of a hierarchical architecture that is comprised of common circuits with similar (and often cortically influenced) functionality. The goal of such systems is to represent sensory observations in a manner that will later facilitate robust pattern classification, mimicking a key attribute of the mammal brain.