| This work is aimed at understanding and modelling the perceptual stability mechanisms of human visual systems, regardless of large changes in the visual sensory input resulting from some visual-related motions. Invariant neuronal representation plays an important role for memory systems to associate and recognize objects.; In contrast to the bulk of previous research work on the learning of invariance that focuses on the pure bottom-up visual information, we incorporate visual-related self-action signals such as commands for eye, head or body movements, to actively collect the changing visual information and gate the learning process. This helps neural networks learn certain degrees of invariance in an efficient way. We describe a method that can produce a network with invariance to changes in visual input caused by eye movements and covert attention shifts. Training of the network is controlled by signals associated with eye movements and covert attention shifting. A temporal perceptual stability constraint is used to drive the output of the network towards remaining constant across temporal sequences of saccadic motions and covert attention shifts. We use a four-layer neural network model to perform the position-invariant extraction of local features and temporal integration of invariant presentations of local features. The model is further extended to handle viewpoint invariance over eye, head, and/or body movements. We also study cases of multiple features instead of single features in the retinal images, which need a self-organized system to learn over a set of feature classes. A modified saliency map mechanism with spatial constraint is employed to assure that attention stays as much as possible on the same targeted object in a multiple-object scene during the first few shifts.; We present results on both simulated data and real images, to demonstrate that our network can acquire invariant neuronal representations, such as position and attention shift invariance. We also demonstrate that our method performs well in realistic situations in which the temporal sequence of input data is not smooth, situations in which earlier approaches have difficulty. |
