Fukang Fang

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Synchronization is an important issue in neuroscience. It is still a problem in computational neuroscience to set and analyze synchronization models, especially the model with complex synaptic equations and burst type neurons. We try to use a new mathematical method and apply a Lyapunov function to the searching for the right synaptic equation, which would(More)
The formation of concepts is a key point in development of human intelligence. There are two kinds of traditional ideas about this problem: hypothesis elimination and associative learning. The Bayesian framework suggested by Tenenbaum absorbs the virtue and avoids the limitations of the two traditional ideas and its results are demonstrated by behavioral(More)
—As we know, there is a debate on the mode of visual perception for a long time, that is, does the visual system acquire percept from local to global or from global to local. A three-layer perceptual model with function of signal decomposition and integration is developed, and Chinese character grapheme is chosen to train the model. The model is trained(More)
The functions of neural system, such as learning, recognition and memory, are the emergences from the elementary dynamic mechanisms. To discuss how the dynamic mechanisms in the neurons and synapses work in the function of recognition, a dynamic neural circuit is designed. In the neural circuit, the information is expressed as the inter-spike intervals of(More)
The cognition and learning of Chinese characters is a good example of human visual perception and learning. The synchronization behavior between neuronal groups in cortical areas is one of the core mechanisms in visual image perception and recognition. We built a neural network with locally coupled Wilson-Cowan oscillators to learn Chinese characters.(More)
There is a key problem that how brain uses the properties of synaptic dynamics to implement its macro-functions. The dynamic synapse model made by J. S. Liaw and T. W. Berger provided an example of implementing speech recognition function with dynamic synaptic mechanisms. The emergence mechanisms of recognition function in the dynamic synapse model are(More)
  • Fukang Fang
  • 2005
Several issues related to learning process are discussed from the viewpoint of self-organization in this paper. Though Hebbian rule and BCM rule are widely used in learning process, there may be a more general mechanism behind the rules and J structure with specific attractors may be such kind of mechanism. Chinese character learning is good example of(More)
In this paper, we explore the recognition of polyphone. The cognition process is complex, which needs other additional information, otherwise it may cause uncertainty in decision. Recent research is almost focused on phonetics, while we plan to explore the question with neu-ral networks. H. Haken used synergetic neural network to discuss the recognition of(More)