Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The compu- tational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.
YANG WenLu1,2, ZHANG LiQing1 & MA LiBo1 1 Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Brain computer interface (BCI) aims at creating new communication channels without depending on brain’s normal output channels of peripheral nerves and muscles. However,natural and sophisticated interactions manner between brain and computer still remain challenging. In this paper,we investigate how the duration of event-related desynchronization/synchronization (ERD/ERS) caused by motor im-agery (MI) can be modulated and used as an additional control parameter beyond simple binary deci-sions. Furthermore,using the non-time-locked properties of sustained (de)synchronization,we have developed an asynchronous BCI system for driving a car in 3D virtual reality environment (VRE) based on cumulative incremental control strategy. The extensive real time experiments confirmed that our new approach is able to drive smoothly a virtual car within challenging VRE only by the MI tasks with-out involving any muscular activities.
