Abstract
Intracortical inhibitory cells play an important role in shaping the simple and complex cells' orientation selectivity. There is, on the other hand, an ample experimental evidence supporting that the intracortical inhibitory cells are orientation selective as well. Recently a computational model for the orientation-specific inhibition has been introduced. The model consists of three parts : (a)a two-layered hierarchical Markov random field ; (b)a computational goal formulated on the Maxirnum-A-Posteriori estimation principle ; (c)a deterministic parallel algorithm to achieve the computational goal. In this article, we introduce a physiologically-plausible firing-rate-coded neural network to show how this computational model can be implemented.
