Sensory perception, learning and integration in neural networks

In biological visual neural networks, one of the prominent features is nonlinear functions, which play important roles in the visual system. However, the order of the nonlinearity of the visual system is one of the unsolved problems in its processing. The non-Fourier motion is visually perceived motion that cannot be explained simply by the autocorrelation (Fourier motion) of the stimulus. This non-Fourier motion is said to perceive it by the pre-processing of the nonlinearity transformation in the visual system. First, we analyze the structure and the function of the nonlinear asymmetric networks in the visual system. Second, sensory integration is realized by the sensor neural networks, which consist of the forward, backward networks and the integration neuron.