Computational modeling of visual selective attention based on correlation and synchronization of neural activity

Abstract

Within the broad area of computational intelligence, it is of great importance to develop new computational models of human behaviour aspects. In this report we look into the recently suggested theory that neural synchronization of activity in different areas of the brain occurs when people attend to external visual stimuli. Furthermore, it is suspected that this cross-area synchrony may be a general mechanism for regulating information flow through the brain. We investigate the plausibility of this hypothesis by implementing a computational model of visual selective attention that is guided by endogenous and exogenous goals (i.e., what is known as top down and bottom-up attention). The theoretical structure of this model is based on the temporal correlation of neural activity that was initially proposed by Niebur and Koch (1994). While a saliency map is created in the model at the initial stages of processing visual input, at a later stage of processing, neural activity passes through a correlation control system which comprises of coincidence detector neurons. These neurons measure the degree of correlation between endogenous goals and the presented visual stimuli and cause an increase in the synchronization between the brain areas involved in vision and goal maintenance. The model was able to simulate with success behavioural data from the “attentional blink” paradigm (Raymond and Sapiro, 1992). This suggests that the temporal correlation idea represents a plausible hypothesis in the quest for understanding attention.