Title :
A group-decision making model of orientation detection
Author :
Wei, Hui ; Ren, Yuan ; Wang, Zheyan
Author_Institution :
Lab. of Cognitive Model & Algorithm, Fudan Univ., Shanghai, China
Abstract :
The feedforward model proposed by Hubel and Wiesel partially explained orientation selectivity in simple cells. This classical hypothesis attributed orientation preference to idealized alignment of geniculate cell receptive fields. Many scholars have been either revising this model or putting forward new theories to account for more related phenomenon such as contrast invariant tuning. None of the previous neural models is complete in implementation details or involves strict computational strategies. This paper mathematically studied a detailed but vital question which has long been neglected: the possibility of massive variable-sized, unaligned geniculate cell receptive fields producing the orientation selectivity of a simple cell. The response curve of each afferent neuron is fully utilized to obtain a local constraint and a group-decision making approach is then applied to solve the constraint satisfaction problem. Our new model does not achieve just consistent experimental results with physiological data, but consistent interpretations of several illusions with observers´ perceptions. The current work, which supplemented the previous models with necessary computational details, is based on ensemble coding in essence. This underlying mechanism helps to understand how visual information is processed in from the retina to the cortex.
Keywords :
cellular biophysics; constraint satisfaction problems; decision making; eye; feedforward neural nets; genetics; physiological models; visual perception; classical hypothesis attributed orientation; constraint satisfaction problem; cortex; ensemble coding; feedforward model; geniculate cell receptive field alignment; group decision making model; orientation detection; orientation selectivity; physiological data; retina; unaligned geniculate cell receptive field; visual information; Computational modeling; Image segmentation; Mathematical model; Neurons; Numerical models; Radio frequency; Visualization;
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
DOI :
10.1109/IJCNN.2012.6252662