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

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.