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

Kerstin Schill; Volker Baier; Florian Roehrbein; Wilfried Brauer

doi:10.1117/12.429535

8 June 2001 Hierarchical network model for the analysis of human spatio-temporal information processing

Kerstin Schill, Volker Baier, Florian Roehrbein, Wilfried Brauer

Proceedings Volume 4299, Human Vision and Electronic Imaging VI; (2001) https://doi.org/10.1117/12.429535
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

Abstract

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 analyzing and modeling the presentation 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 base don Kohonens SOMs. In order to enable the representation, processing and prediction of spatio-temporal pattern on different levels of granularity and abstraction the SOMs are organized in a hierarchical manner. The model has the advantage of a 'self-teaching' learning algorithm and stored temporal information try local feedback in each computational layer. The constraints for the neural modeling and data set for training the neural network are obtained by psychophysical experiments where human subjects' abilities for dealing with spatio-temporal information is investigated.

Citation Download Citation

Kerstin Schill, Volker Baier, Florian Roehrbein, and Wilfried Brauer "Hierarchical network model for the analysis of human spatio-temporal information processing", Proc. SPIE 4299, Human Vision and Electronic Imaging VI, (8 June 2001); https://doi.org/10.1117/12.429535

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