A model of computation in neocortical architecture

Abstract

We propose that the specific architecture of the neocortex reflects the organization principles of neocortical computation. In this paper, we place the anatomically defined concept of columns into a functional context. It is provided by a large-scale computational hypothesis on visual recognition, which includes both, rapid parallel forward recognition, independent of any feedback prediction, and a feedback controlled refinement system. Short epochs of periodic clocking define a global reference time and introduce a discrete time for cortical processing which enables the combination of parallel categorization and sequential refinement. The presented model differs significantly from conventional neural network architectures and suggests a novel interpretation of the role of gamma oscillations and cognitive binding.