We introduce a neural model capable of feature selectiveness by spike-mediated synchronization through lateral synaptic couplings. For a stimulus containing two features, the attended one elicits a higher response. In the case of sequential single-feature stimuli, repetition of the attended feature also results in an enhanced response, exhibited by greater synchrony and higher spiking rates.

Models for perceptual grouping and contour integration are presented. Connection weights depend on distances and angle differences, while neurons evolve following a spiking dynamics (Izhikevich’s model in most of the considered cases). Although the studied synapses depend on discrete three-valued functions, simulations display the emergence of approximate synchrony, making these cognitive tasks possible. Noise effects are examined, and the possibility of achieving similar results with a different neuron model is discussed.