DIFFUSION-CONCENTRATION NEURAL SYSTEM FOR SEPARATING FIGURES FROM BACKGROUND

According to psychology, diffusion and concentration of neural activit y is a basic law of the neuronal interaction in the cerebral neocortex . There exists evidence that the lateral connections between cells in the cerebral cortex take the form that the short-range lateral connect ions are excitatory and the long-range ones are inhibitory [12]. This paper shows that the positive short-range connections perform the diff usional function, while the negative long-range ones perform the conce ntrative function. Diffusion and concentration are two reverse and coe xistent neural processes whose relative strength can be controlled by these short-range or long-range connections. When the diffusion is str ong relative to the concentration, all neurons in the whole cortex kee p low and homogeneous active levels; when the concentration is stronge r, the active level of these around the focus of the concentration wil l rise sharply. The diffusion and concentration are perhaps an essenti al neural mechanism that occurs at different stages of vision. This pa per uses the mechanism to separate figures from background and present s a neural structure, called the diffusion-concentration network (DCN) . DCN is a 2-D array of neurons with both the positive variable short- range and negative constant long-range lateral connections. Attention which serves as the top-down input of DCN is the diffusional signal, t hat is, the diffusional source, and spreads over the network. Edges in formation which serves as the bottom-up input is the blocking signal a nd inhibits the positive short-range connections to block the contour- sensitive diffusive process. As a result, in the positions across edge s the diffusion becomes weak and the concentration becomes dominant. T he diffusion-blocking process has the active level of the neurons with in a contour rise, and further it will give the directions of concentr ation and instruct the active potential of neurons in transfer from th e neurons in the background region to those in the figure regions. The concentration embodies the psychophysical result that figures and bac kground are in competition with each other. The competition makes both figures strengthened and background suppressed. The computer simulati on of the network is given. The difference from previous approaches is discussed.