Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Adaptive resonance theory (ART)
The handbook of brain theory and neural networks
A control model of the movement of attention
Neural Networks
A neural network model of Parkinson's disease bradykinesia
Neural Networks
2006 Special Issue: Attention as a controller
Neural Networks
Attention links sensing to recognition
Image and Vision Computing
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
A biophysical model of decision making in an antisaccade task through variable climbing activity
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Neural model of dopaminergic control of arm movements in parkinson’s disease bradykinesia
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
| Hi-index | 0.01 |
A novel brain inspired cognitive system architecture of an active visual search model is presented. The model is multi-modular consisting of spatial and object visual processing, attention, reinforcement learning, motor plan and motor execution modules. The novelty of the model lies on its decision making mechanisms. In contrast to previous models, decisions are made from the interplay of a winner-take-all mechanism in the spatial, object and motor salient maps between the resonated by top-down attention and bottom-up visual feature extraction and salient map formation selectively tuned by a reinforcement signal spatial, object and motor representations, and a reset mechanism due to inhibitory feedback input from the motor execution module to all other modules. The reset mechanism due to feedback inhibitory signals from the motor execution module to all other modules suppresses the last attended location from the saliency map and allows for the next gaze to be executed.