Emergence of orientation-selective inhibition in the primary visual cortex: a Bayes-Markov computational model.

Abstract

The recent consensus is that virtually all aspects of response selectivity exhibited by the primary visual cortex are either created or sharpened by cortical inhibitory interneurons. Experimental studies have shown that there are cortical inhibitory cells that are driven by geniculate cells and that, like their cortical excitatory counterparts, are orientation selective, though less sharply tuned. The main goal of this article is to demonstrate how orientation-selective inhibition might be created by the circuitry of the primary visual cortex (striate cortex, V1) from its nonoriented geniculate inputs. To fulfill this goal, first, a Bayes-Markov computational model is developed for the V1 area dedicated to foveal vision. The developed model consists of three parts: (i) a two-layered hierarchical Markov random field that is assumed to generate the activity patterns of the geniculate and cortical inhibitory cells, (ii) a Bayesian computational goal that is formulated based on the maximum a posteriori (MAP) estimation principle, and (iii) an iterative, deterministic, parallel algorithm that leads the cortical circuitry to achieve its assigned computational goal. The developed model is not fully LGN driven and it is not implementable by the neural machinery of V1. The model, then, is transformed into a fully LGN-driven and physiologically plausible form. Computer simulation is used to demonstrate the performance of the developed models.