A neural network model for visual selection and shifting.
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.
['Qiao|Yuanhua|Y|', 'Liu|Xiaojie|X|', 'Miao|Jun|J|', 'Duan|Lijuan|L|']
D001288:Attention D002540:Cerebral Cortex D016571:Neural Networks, Computer D009474:Neurons D010507:Periodicity D012160:Retina D014796:Visual Perception
neural network Visual selection and shifting mapping dynamic system periodic activity synchronization