Title :
Perception and prediction — A connectionist model
Author :
Iyer, Laxmi R. ; Seng-Beng Ho
Author_Institution :
Temasek Labs., Nat. Univ. of Singapore, Singapore, Singapore
Abstract :
Generating appropriate responses to incoming stimuli is a fundamental task of an organism. However, in order to generate intelligent responses, it is important to have a deeper understanding of the environment, and make predictions based on this knowledge. Although the ability to make predictions is intrinsic in humans and many animals, it is still a difficult task for a machine with no in built knowledge about the situation. In this paper we present a biologically inspired neural network model that predicts the future trajectory of a moving object after observing its current trajectory.
Keywords :
neural nets; biologically inspired neural network; connectionist model; moving object trajectory; perception; prediction; Animals; Biological system modeling; Brain modeling; Hippocampus; Predictive models; Trajectory;
Conference_Titel :
Computational Intelligence for Human-like Intelligence (CIHLI), 2013 IEEE Symposium on
Conference_Location :
Singapore
DOI :
10.1109/CIHLI.2013.6613261