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A Neural Network Model for the Estimation of Time-to-Collision

  • Ling Wang
  • Hongjin Sun
  • Dezhong Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

Artificial Neural Networks (ANNs) which are derived from Biological Neural Networks (BNNs) are enhanced by many advanced mathematical techniques and have become powerful tools for solving complicated engineering problems. Integrating BNNs with mature ANNs is a very effective method of solving intricate biological problems and explaining neurophysiological data. In this paper we propose a neural network model that explains how the brain processes visual information about impending collisions with an object – in particular, how time-to-collision information is caculated in the brain. The model performs extremely well as a result of incorporating physiological data with the methods involved in the development of ANNs. By implementing this novel compuational neural network model, the results of the simulation demonstrate that this integrative approach is a very useful and efficient way to deal with complicated problems in neural computation.

Keywords

Neural Network Neural Network Model Retinal Image Optic Tectum Neurophysiological Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Wang
    • 1
  • Hongjin Sun
    • 2
  • Dezhong Yao
    • 1
  1. 1.Center of NeuroInformatics, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Department of Psychology, Neuroscience and BehaviourMcMaster UniversityHamiltonCanada

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