A deterministic biologically plausible classifier

Regarding biological visual classification, recent series of experiments have enlightened that data classification can be realized in the human visual cortex with latencies of about 100 ms , which, considering the visual pathways latencies, is only compatible with a very specific processing architecture, described by the so-called Thorpe model. Surprisingly enough, this experimental evidence is in coherence with algorithms derived from the statistical learning theory, following the work of Vapnik. More precisely, there is a double link: on one hand, the Vapnik theory offers tools to evaluate and analyze the Thorpe model performances and on the other hand, this model is an interesting front-end for algorithms derived from the Vapnik theory. The present contribution develops this idea and experiments its performances using a tiny sign language recognition experiment.