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Author:Franovic, Tin
Title:Cortex inspired network architectures for spatio-temporal information processing
Publication type:Master's thesis
Publication year:2013
Pages:v + 95      Language:   eng
Department/School:Perustieteiden korkeakoulu
Main subject:Informaatiotekniikka   (T-61)
Supervisor:Herman, Pawel ; Vigário, Ricardo
Instructor:Herman, Pawel
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Reading digital theses in the closed network of the Aalto University Harald Herlin Learning Centre

In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/

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Location:P1 Ark Aalto     | Archive
Keywords:cortical models
large-scale data analysis
spatio-temporal recognition
speech recognition
brain signal processing
Abstract (eng): The abundance of high-dimensional datasets provides scientists with a strong foundation in their research.
With high-performance computing platforms becoming increasingly available and more powerful, large-scale data processing represents an important step toward modelling and understanding the underlying processes behind such data.

In this thesis, we propose general cortex-inspired information processing network architecture capable of capturing spatio-temporal correlations in data and forming distributed representations as cortical activation patterns.
The proposed architecture has a modular and multi-layered organization which is efficiently parallelized to allow large-scale computations.
The network allows unsupervised processing of multivariate stochastic time series, irregardless of the data source, producing a sparse de-correlated representation of the input features expanded by time delays.

The features extracted by the architecture are then used for supervised learning with Bayesian confidence propagation neural networks and evaluated on speech classification and recognition tasks, exploited auditory signals for speech recognition as a use case for performance evaluation.
In terms of classification performance, the proposed architecture outperforms modern machine-learning methods such as support vector machines and obtains results comparable to other state-of-the-art speech recognition methods.
The potential of the proposed scalable cortex-inspired approach to capture meaningful multivariate temporal correlations and provide insight into the model-free high-dimensional data decomposition basis is expected to be of particular use in the analysis of large brain signal datasets such as EEG or MEG.
ED:2014-01-13
INSSI record number: 48332
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