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From ANN to Biomimetic Information Processing
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-2358-7815
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
2009 (English)In: BIOLOGICALLY INSPIRED SIGNAL PROCESSING FOR CHEMICAL SENSING / [ed] Gutierrez A, Marco S, 2009, Vol. 188, 33-43 p.Conference paper, Published paper (Refereed)
Abstract [en]

Artificial neural networks (ANN) are useful components in today's data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits.

Place, publisher, year, edition, pages
2009. Vol. 188, 33-43 p.
Series
Studies in Computational Intelligence, ISSN 1860-949X
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-30835DOI: 10.1007/978-3-642-00176-5_2ISI: 000266719500002Scopus ID: 2-s2.0-62549146707ISBN: 978-3-642-00175-8 (print)OAI: oai:DiVA.org:kth-30835DiVA: diva2:403007
Conference
OSPEL Workshop on Bio-inspired Signal Processing Barcelona, SPAIN, 2007
Note
QC 20110310Available from: 2011-03-10 Created: 2011-03-04 Last updated: 2013-05-15Bibliographically approved
In thesis
1. On large-scale neural simulations and applications in neuroinformatics
Open this publication in new window or tab >>On large-scale neural simulations and applications in neuroinformatics
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis consists of three parts related to the in silico study of the brain: technologies for large-scale neural simulations, neural algorithms and models and applications in large-scale data analysis in neuroinformatics. All parts rely on the use of supercomputers.

A large-scale neural simulator is developed where techniques are explored for the simulation, analysis and visualization of neural systems on a high biological abstraction level. The performance of the simulator is investigated on some of the largest supercomputers available.

Neural algorithms and models on a high biological abstraction level are presented and simulated. Firstly, an algorithm for structural plasticity is suggested which can set up connectivity and response properties of neural units from the statistics of the incoming sensory data. This can be used to construct biologically inspired hierarchical sensory pathways. Secondly, a model of the mammalian olfactory system is presented where we suggest a mechanism for mixture segmentation based on adaptation in the olfactory cortex. Thirdly, a hierarchical model is presented which uses top-down activity to shape sensory representations and which can encode temporal history in the spatial representations of populations.

Brain-inspired algorithms and methods are applied to two neuroinformatics applications involving large-scale data analysis. In the first application, we present a way to extract resting-state networks from functional magnetic resonance imaging (fMRI) resting-state data where the final extraction step is computationally inexpensive, allowing for rapid exploration of the statistics in large datasets and their visualization on different spatial scales. In the second application, a method to estimate the radioactivity level in arterial plasma from segmented blood vessels from positron emission tomography (PET) images is presented. The method outperforms previously reported methods to a degree where it can partly remove the need for invasive arterial cannulation and continuous sampling of arterial blood during PET imaging.

In conclusion, this thesis provides insights into technologies for the simulation of large-scale neural models on supercomputers, their use to study mechanisms for the formation of neural representations and functions in hierarchical sensory pathways using models on a high biological abstraction level and the use of large-scale, fine-grained data analysis in neuroinformatics applications.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. vi, 66 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2013:06
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-122190 (URN)978-91-7501-776-1 (ISBN)
Public defence
2013-06-03, F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20130515

Available from: 2013-05-15 Created: 2013-05-14 Last updated: 2017-08-15Bibliographically approved

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