Evolving spiking neural networks for adaptive audiovisual pattern recognition

dc.contributor.advisorKasabov, Nikola
dc.contributor.advisorBenuskova, Lubica
dc.contributor.authorWysoski, Simei Gomes
dc.description.abstractThis dissertation presents new modular and integrative information methods and systems inspired by the way the brain performs information processing, in particular, pattern recognition. The proposed artificial systems use spiking neurons as basic elements, which are the key components of spiking neural networks. Of particular interest to this research are various spiking neural network architectures and learning procedures that permit different pattern recognition problems to be solved in an evolvable and adaptive way. Spiking neural networks are used to model human visual and auditory pathways and are trained to perform the specific task of person authentication. The systems are individually tuned and trained to recognize facial information and to analyze sound signals from spoken sentences. The modelling of the integration of different sources of information (multisensory integration) using spiking neural networks is also a subject of investigation. A network architecture is proposed and a model for audiovisual pattern recognition is designed as an example. The main original contributions of this thesis are: a) Evaluation and further extension of adaptive learning procedures to perform visual pattern recognition. A new learning procedure that enables the system to change its structure, creating/merging neuronal maps of spiking neurons is presented and evaluated on a face recognition problem. b) Design of two new spiking neural network architectures to perform person authentication through the processing of speech signals. c) Design and evaluation of a new architecture that integrates sensory modalities based on spiking neurons. The integrative architecture combines opinions from individual modalities within a supramodal layer, which contains neurons sensitive to multiple sensory information. An additional feature that increases biological relevance is the crossmodal coupling of modalities, which effectively enables a given sensory modality to exert direct influence upon the processing areas typically related to other modalities. The contributions were published in one journal paper and in four refereed international conference proceedings. The proposed system designs were implemented and, through computer simulations, demonstrated comparable performance with traditional benchmarking methods. The systems have some promising features: they can be naturally optimized in respect to different criteria: accuracy (when very accurate results are expected), energy efficiency (when management of resources play an important role), and speed (when a decision needs to be made within a limited time). In this thesis, most of the parameters have been exhaustively optimized by hand or by using simple heuristics. As a direction for future work, there is an opportunity to include automated, specially tailored parameters optimization procedures or even general-purpose optimization algorithms, e.g., Genetic Algorithms and Particle Swarm Optimization. Overall, the results obtained in this thesis clearly indicate that it is indeed possible to have fast and accurate adaptive pattern recognition systems scalable for multiple modalities computing with simple models of spiking neurons. However, it is important to advance the theory of spiking neurons to take advantage of its biological relevance to reach similar or better performance when compared to the human brain, for instance, exploring new neuron models, information coding schemes and network connectivity.
dc.publisherAuckland University of Technology
dc.subjectArtificial intelligence
dc.subjectVisual pattern recognition
dc.subjectAuditory pattern recognition
dc.subjectExperimentation and quantitative evaluation
dc.titleEvolving spiking neural networks for adaptive audiovisual pattern recognition
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelDoctoral Theses
thesis.degree.nameDoctor of Philosophy
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