Algorithm

Linguistics

Cognitive psychology

Philosophy

Neuroscience

Excitatory postsynaptic potential

Contrast (vision)

Cartography

Lateral inhibition

Inhibitory postsynaptic potential

Computation

Selection (genetic algorithm)

Computer science

Cognitive science

Psychology

Pattern recognition (psychology)

Politics

Geography

Political science

Feature selection

Feature (linguistics)

Visual processing

Artificial intelligence

Representation (politics)

Intersection (aeronautics)

Artificial neural network

Perception

Winner-take-all

Frontiers in Psychology

Huang and Pashler [2007, Psychological Review, 114(3), 599-631] suggested that feature-based attention creates a special form of spatial representation, which is termed a Boolean map. It partitions the visual scene into two distinct and complementary regions: selected and not selected. Here, we developed a model of a recurrent competitive network that is capable of state-dependent computation. It selects multiple winning locations based on a joint top-down cue. We augmented a model of the WTA circuit that is based on linear-threshold units with two computational elements: dendritic nonlinearity that acts on the excitatory units and activity-dependent modulation of synaptic transmission between excitatory and inhibitory units. Computer simulations showed that the proposed model could create a Boolean map in response to a featured cue and elaborate it using the logical operations of intersection and union. In addition, it was shown that in the absence of top-down guidance, the model is sensitive to bottom-up cues such as saliency and abrupt visual onset.

A Neurodynamic Model of Feature-Based Spatial Selection