I completed my Masters in electrical engineering from the Department of
Automation at the Technical University of Denmark
(DTU) in 1984 with the thesis entitled "Adaptive Observere og Parametrisk
Identifikation" (in Danish). Then I worked in a research and development
group at Danfoss A/S (private company) until 1991, mostly with analysis
of dynamic control systems, adaptive control theory and program development
(analysis, design and implementation) for microprocessor systems and process
I was then fortunate to get a Ph.D. scholarship from the Danish Computational
Neural Network Center, CONNECT, starting in September 1991, and was enrolled
as a Ph.D student at the Electronics Institute, Technical University of
Denmark (now Section for Digital Signal Processing Department of Informatics and Mathematical Modelling (IMM) at DTU). My major interests during
the study was optimization of Neural Network Models for Time Series Analysis
and Classification purposes. I finished my Ph.D study December 31. 1994.
The title of the thesis is Neural
Networks for Signal Processing.
January 1994 I moved to the Neurobiology Research Unit at Rigshospitalet
where I am working as an Associated Researcher, with Signal Processing
and Mathematical Modeling (especially, Artificial Neural Network techniques)
within the field of brain modeling.
My major scientific interests are within signal processing, especially
modeling of of brain data. More specifically I have focused on the following
The different optimization schemes for artificial neural network's can
in general also be used for optimization of other types of linear and non-linear
models. In the area of modeling medical datasets these are very important
while almost always very limited datasets are present.
In this area I have mostly worked with estimation of rateconstants
in kinetic models of the brain. Estimation of the rateconstants has been
based on dynamic FDG PET data. Especially, I have worked with estimation
of the rateconstants in Sokoloff's model at a pixel by pixel basis using
an Artificial Neural Network technique for improving the speed.
PET and fMRI comparison
My contribution to this area has mostly been in analyzing and comparing
some water PET and BOLD fMRI datasets. The analysis techniques that has
been used is based on the SPM (Statistical Parametric Model/Generalized
Linear Model) and SSM (Scaled Subprofile Model).
Within this area I have mainly worked with different methods for estimating
the generalization ability for linear and especially non-linear mathematical
models. (The generalization performance is here defined as the model performance
at a dataset that hasn't been used for training model parameters.)
Optimization of Artificial Neural Network models
In the design and optimization process for artificial neural network
models both the architecture (the number of connections between input,
output and neurons) and the parameters (weights) for the connections. I
have in this area worked with optimization schemes for especially, feed-forward
neural networks, that optimizes the architecture (prunes connections from
the structure) so the models are well fitted to a given problems, with
respect to the generalization performance.
Optimization of training schemes for Artificial Neural Networks
In a optimization scheme for neural network parameters different ``hyperparameters''
has to be optimized for achieving good generalization performance. Different
methods for tuning these parameters by dividing the dataset in training
and validation datasets has been evaluated.
Abstracts (and for some of the articles a full postscript version of the
manuscript) can be retrieved from our publication
database. A list of recent
publications can be retrieved from the NRU publication database.
In my work at NRU I am involved in projects and collaborating with people
from the following groups:
from which I interact in particular with the Section
for Digital Signal Processing, Department for Mathematical Modelling,
Technical University of Denmark, and the PET
Imaging Service, Minneapolis VA Medical Center, Minneapolis, USA.
I work as a system administrator for the computer laboratory in the Neurobiology
Research Unit. This lab is equipped with a number of Hewlett Packard workstations
for analysis and visualization of datasets. All workstations are connected
in a network for easy transportation of PET and fMRI datasets.
Further, all office PC's are connected via the computer network to the
workstations for easy access to image's, analysis results, library databases,
As the coordinator of the Functional
Imaging Group (FIG), I accept general comments and ideas for new presentations.
I live in Hørsholm a smaller city 25 km. north of Copenhagen. In
the summertime I enjoy going for a sailing trip at Øresund. This
is relaxation for body and soul.
If you want to contact me you can either send me an email at claus.svarer#nru.dk,
or use the street address below.
Neurobiology Research Unit
Phone: (+45) 3545 6716
Fax: (+45) 3545 6713