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      Oscillatory synchronization model of attention to moving objects

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      Author
      Yilmaz, O.
      Date
      2012
      Source Title
      Neural Networks
      Print ISSN
      0893-6080
      Publisher
      Elsevier
      Volume
      29-30
      Pages
      20 - 36
      Language
      English
      Type
      Article
      Item Usage Stats
      156
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      113
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      Abstract
      The world is a dynamic environment hence it is important for the visual system to be able to deploy attention on moving objects and attentively track them. Psychophysical experiments indicate that processes of both attentional enhancement and inhibition are spatially focused on the moving objects; however the mechanisms of these processes are unknown. The studies indicate that the attentional selection of target objects is sustained via a feedforward-feedback loop in the visual cortical hierarchy and only the target objects are represented in attention-related areas. We suggest that feedback from the attention-related areas to early visual areas modulates the activity of neurons; establishes synchronization with respect to a common oscillatory signal for target items via excitatory feedback, and also establishes de-synchronization for distractor items via inhibitory feedback. A two layer computational neural network model with integrate-and-fire neurons is proposed and simulated for simple attentive tracking tasks. Consistent with previous modeling studies, we show that via temporal tagging of neural activity, distractors can be attentively suppressed from propagating to higher levels. However, simulations also suggest attentional enhancement of activity for distractors in the first layer which represents neural substrate dedicated for low level feature processing. Inspired by this enhancement mechanism, we developed a feature based object tracking algorithm with surround processing. Surround processing improved tracking performance by 57% in PETS 2001 dataset, via eliminating target features that are likely to suffer from faulty correspondence assignments. © 2012 Elsevier Ltd.
      Keywords
      Attention
      Cortical oscillations
      Neural synchrony
      Object tracking
      Computational neural networks
      Data sets
      Desynchronization
      Dynamic environments
      Enhancement mechanism
      Feature-based
      Inhibitory feedback
      Integrate-and-fire neurons
      Low-level features
      Modeling studies
      Moving objects
      Neural activity
      Neural substrates
      Object tracking algorithm
      Oscillatory signals
      Oscillatory synchronization
      Psychophysical experiments
      Target feature
      Target object
      Tracking performance
      Two layers
      Visual areas
      Visual cortical
      Visual systems
      Brain
      Neural networks
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      http://hdl.handle.net/11693/21483
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.neunet.2012.01.005
      Collections
      • Department of Psychology 170
      • National Magnetic Resonance Research Center (UMRAM) 198
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