Evolving spiking neural networks for audiovisual information processing.
This paper presents a new modular and integrative sensory information system inspired by the way the brain performs information processing, in particular, pattern recognition. Spiking neural networks are used to model human-like visual and auditory pathways. This bimodal system is trained to perform the specific task of person authentication. The two unimodal systems are individually tuned and trained to recognize faces and speech signals from spoken utterances, respectively. New learning procedures are designed to operate in an online evolvable and adaptive way. Several ways of modelling sensory integration using spiking neural network architectures are suggested and evaluated in computer experiments.
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 1051 Auckland, New Zealand. wysoski@hotmail.com
Simei Gomes
SG
Wysoski
Lubica
L
Benuskova
Nikola
N
Kasabov
D001306:Auditory Pathways / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D046709:Pattern Recognition, Physiological / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology*