title: A neural network model for visual selection and shifting.
abstract: In this paper, a two-layer network is built to simulate the mechanism of visual selection and shifting based on the mapping dynamic model for instantaneous frequency. Unlike the differential equation model using limit cycle to simulate neuron oscillation, we build an instantaneous frequency mapping dynamic model to describe the change of the neuron frequency to avoid the difficulty of generating limit cycle. The activity of the neuron is rebuilt based on the instantaneous frequency and in this work, we use the first layer of neurons to implement image segmentation and the second layer of neurons to act as visual selector. The frequency of the second neuron (central neuron) is always changing, while central neuron resonates with the neurons corresponding to an object, the object is selected, then with the central neuron frequency changing, the selected object loses attention, the process goes on.
authors: * College of Applied Sciences, Beijing University of Technology, Beijing 100124, P. R. China.
authors: Yuanhua
authors: Y
authors: Qiao
authors: * College of Applied Sciences, Beijing University of Technology, Beijing 100124, P. R. China.
authors: Xiaojie
authors: X
authors: Liu
authors: † Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China.
authors: Jun
authors: J
authors: Miao
authors: ‡ College of Computer Science, Beijing University of Technology, Beijing 100124, P. R. China.
authors: Lijuan
authors: L
authors: Duan
mesh_terms: D001288:Attention / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D010507:Periodicity; D012160:Retina / Q000502:physiology; D014796:Visual Perception / Q000502:physiology
keywords: Visual selection and shifting; mapping dynamic system; neural network; periodic activity; synchronization