Invariant representations of visual patterns in a temporal population code

Mammalian visual systems are characterized by their ability to recognize stimuli invariant to various transformations. Here, we investigate the hypothesis that this ability is achieved by the temporal encoding of visual stimuli. By using a model of a cortical network, we show that this encoding is invariant to several transformations and robust with respect to stimulus variability. Furthermore, we show that the proposed model provides a rapid encoding, in accordance with recent physiological results. Taking into account properties of primary visual cortex, the application of the encoding scheme to an enhanced network demonstrates favorable scaling and high performance in a task humans excel at.