Abstract
Deep feed-forward convolutional neural networks
(CNNs) have become ubiquitous in virtually all machine
learning and computer vision challenges; however, advancements in CNNs have arguably reached an engineering
saturation point where incremental novelty results in minor
performance gains. Although there is evidence that object
classification has reached human levels on narrowly defined
tasks, for general applications, the biological visual system
is far superior to that of any computer. Research reveals
there are numerous missing components in feed-forward
deep neural networks that are critical in mammalian vision.
The brain does not work solely in a feed-forward fashion,
but rather all of the neurons are in competition with each
other; neurons are integrating information in a bottom up
and top down fashion and incorporating expectation and
feedback in the modeling process. Furthermore, our visual
cortex is working in tandem with our parietal lobe, integrating sensory information from various modalities