Neural mechanisms of selective attention: a computational model

In cluttered environments, efficient vision depends critically on appropriate attentional selection. Selective attention primes the processing of behaviorally relevant objects so they can influence behavior in spite of
more salient surrounding stimuli. Selective visual attention is known to affect neural activity in extrastriate visual areas by selectively increasing the gain of neuronal responses and by biasing competition between stimuli representations. In addition, recent experiments show that attention enhances
the selectivity of population activity, and its synchrony. The integration of all this evidence in a single conceptual framework remains elusive, and little is known about the precise neurophysiological mechanisms involved. I will show through a biophysical computational model of two reciprocally
connected brain areas how all this phenomenology can be accounted for by a
top-down input from a working memory area that stores items in persistent
activity.