A hierarchical network model for the analysis of human spatio-temporal information processing

The perception of spatio-temporal pattern is a fundamental part of visual cognition. In order to understand more about the principles behind these biological processes, we are analysing and modeling the representation of spatio-temporal structures on different levels of abstraction. For the low-level processing of motion information we have argued for the existence of a spatio-temporal memory in early vision. The basic properties of this structure are reflected in a neural network model which is currently developed. Here we discuss major architectural features of this network which is based on Kohonens SOMs (self organizing maps). In order to enable the representation, processing and prediction of spatio-temporal pattern on different levels of granularity and abstraction the SOM’s are organized in a hierarchical manner. The model has the advantage of a “self-teaching” learning algorithm and stores temporal information by local feedback in each computational layer. The constraints for the neural modeling and the data sets for training the neural network are obtained by psychophysical experiments where human subjects’ abilities for dealing with spatio-temporal information is investigated.