A topological and temporal correlator network for spatiotemporal pattern learning, recognition, and recall

Abstract

In this paper, we describe the design of an artificial neural network for spatiotemporal pattern recognition and recall. This network has a five-layered architecture and operates in two modes: pattern learning and recognition mode, and pattern recall mode. In pattern learning and recognition mode, the network extracts a set of topologically and temporally correlated features from each spatiotemporal input pattern based on a variation of Kohonen's self-organizing maps. These features are then used to classify the input into categories based on the fuzzy ART network. In the pattern recall mode, the network can reconstruct any of the learned categories when the appropriate category node is excited or probed. The network performance was evaluated via computer simulations of time-varying, two-dimensional and three-dimensional data. The results show that the network is capable of both recognition and recall of spatiotemporal data in an on-line and self-organized fashion. The network can also classify repeated events in the spatiotemporal input and is robust to noise in the input such as distortions in the spatial and temporal content.