Winner-take-all (WTA) competition is an important computational principle in the brain, by which neurons can compete with each other for activation.Through configuring its network parameters, the WTA neural network can achieve various dynamic characteristics of attractors underlying many visual cognitive functions, such as decision making, working memory and hysteresis in visual perceptions.Therefore, physical implementations of the WTA network with the N-methyl-D-aspartate receptors (NMDARs) in silicon is of benefit to design elementary neuromorphic modules of these visual cognitive functions.In our work, we developed a WTA circuit with NMDARs with desired dynamics through the dynamical system approach of circuit synthesis according to the two-variable version of a biophysical plausible WTA model.Through comparing results from circuit simulations in Cadence and theoretical analysis in dynamical systems approach, we demonstrated that the WTA circuit we build could (1) implement decision task and reproduce ramping neural activities observed in the electrophysiological recording in monkcys experiments as attractor dynamics of the WTA model predicts, (2) reproduce the sustained activities and the limited memory capacity during working memory tasks, (3) achieve hysteresis observed in the visual perception and operatefollowing the theoretical analysis for the WTA model.Neuromorphic implementation of these visual cognitive functions in the WTA circuit will be benefit to develop new generation of technologies that carry out brain-like artificial intelligence while maintaining remarkable energy efficiency.