A neural network computational model of visual selective attention
Date Issued
2009
DOI
10.1007/978-3-642-03969-0_32
Abstract
One challenging application for Neural Networks would be to try and
actually mimic the behaviour of the system that has inspired their creation as
computational algorithms. That is to use Neural Networks in order to simulate
important brain functions. In this report we attempt to do so, by proposing a Neural
Network computational model for simulating visual selective attention, a specific
aspect of human attention. The internal operation of the model is based on recent
neurophysiologic evidence emphasizing the importance of neural synchronization
between different areas of the brain. Synchronization of neuronal activity has been
shown to be involved in several fundamental functions in the brain especially in
attention. We investigate this theory by applying in the model a correlation control
module comprised by basic integrate and fire model neurons combined with
coincidence detector neurons. Thus providing the ability to the model to capture the
correlation between spike trains originating from endogenous or internal goals and
spike trains generated by the saliency of a stimulus such as in tasks that involve top –
down attention (Cobetta and Shulman, 2002). The theoretical structure of this model
is based on the temporal correlation of neural activity as initially proposed by Niebur
and Koch (1994). More specifically; visual stimuli are represented by the rate and
temporal coding of spiking neurons. The rate is mainly based on the saliency of each
stimuli (i.e. brightness intensity etc.) while the temporal correlation of neural activity
plays a critical role in a later stage of processing were neural activity passes through
the correlation control system and based on the correlation, the corresponding neural
activity is either enhanced or suppressed. In this way, attended stimulus will cause an
increase in the synchronization as well as additional reinforcement of the
corresponding neural activity and therefore it will “win” a place in working memory.
We have successfully tested the model by simulating behavioural data from the
“attentional blink” paradigm (Raymond and Sapiro, 1992).
actually mimic the behaviour of the system that has inspired their creation as
computational algorithms. That is to use Neural Networks in order to simulate
important brain functions. In this report we attempt to do so, by proposing a Neural
Network computational model for simulating visual selective attention, a specific
aspect of human attention. The internal operation of the model is based on recent
neurophysiologic evidence emphasizing the importance of neural synchronization
between different areas of the brain. Synchronization of neuronal activity has been
shown to be involved in several fundamental functions in the brain especially in
attention. We investigate this theory by applying in the model a correlation control
module comprised by basic integrate and fire model neurons combined with
coincidence detector neurons. Thus providing the ability to the model to capture the
correlation between spike trains originating from endogenous or internal goals and
spike trains generated by the saliency of a stimulus such as in tasks that involve top –
down attention (Cobetta and Shulman, 2002). The theoretical structure of this model
is based on the temporal correlation of neural activity as initially proposed by Niebur
and Koch (1994). More specifically; visual stimuli are represented by the rate and
temporal coding of spiking neurons. The rate is mainly based on the saliency of each
stimuli (i.e. brightness intensity etc.) while the temporal correlation of neural activity
plays a critical role in a later stage of processing were neural activity passes through
the correlation control system and based on the correlation, the corresponding neural
activity is either enhanced or suppressed. In this way, attended stimulus will cause an
increase in the synchronization as well as additional reinforcement of the
corresponding neural activity and therefore it will “win” a place in working memory.
We have successfully tested the model by simulating behavioural data from the
“attentional blink” paradigm (Raymond and Sapiro, 1992).
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