Structuring global responses of local filters using lateral connections
This paper reviews an unsupervised artificial neural network that has been shown to perform principal component analysis and a constrained version of the same network that has been shown to perform factor analysis. It is shown that this network, when trained on real video data, finds filters that are both local in time and local in space. It is further shown that the type of movement and environment in these video sequences determines the shape of the filters found. It is then shown that lateral connections derived from finding the mode of a probability density function can be used to form a global ordering of the output responses of the network and that different parameter regimes can be used to differentiate between competing, mutually interfering classes of factors. Indeed, some parameter values can be used to suppress entirely particular classes of factors. The net effect on video data is that specific types of movement can be identified by examining the network's outputs’ responses.