S. Haykin, Adaptive filter theory, Pren-
tice Hall, 2001;
H.P. Hsu, Signals and systems,
McGraw Hill, 1995;
L. O. Chua and T. Roska, Cellular neu-
ral networks and visual computing:
foundation and applications. Cambridge
Univ Pr, 2002;
S. Haykin, Neural networks: a compre-
hensive foundation, McGraw Gill,
1999.
However, the references listed above only
provide some initial knowledge, but do not
provide that comprehensive foundation which
is the objective of the course. In our approach
the course presents integral foundations which
combine the different principles and equip the
students with a deeper understanding of the
conceptual issues of signal processing. This
goal cannot be reached by using international
textbooks but needs the background of special
expertise of the course instructors.
One of the principal research areas of Prof.
Dr. Levendovszky János is focusing on adap-
tive digital signal processing algorithms and
on neural based signal processing. He is and
has been the principal investigator of numer-
ous international research projects on the field.
He has international teaching experience in
signal processing giving courses at different
universities of USA, South Korea, and Europe.
Dr. Oláh András PhD has developed novel
nonlinear signal processing algorithms for
digital communication systems.
The new course is based on a 7-year expe-
rience of teaching similar subjects and having
developed not only lecture series but class-
room exercises as well.
III.
R
ESULTS
The course material is divided into 12 parts,
given as follows:
1. Introduction and Analog to Digital con-
version.
2. Description digital signals and systems
in time domain.
3. Description digital signals and systems
in transform (Z, DFT) domain.
4. Efficient computation of the transform
domain (FFT) and filter design.
5. Adaptive signal processing.
6. Introduction to neural processing (inspi-
ration, history and approaches).
7. Signal processing by a single neuron
(linear set separation).
8. Hopfield network, Hopfield net as asso-
ciative memory and combinatorial op-
timizer.
9. Cellular Neural Network.
10. Feedforward Neural Networks (general-
ization, representation, learning, appl.).
11. Principal Component Analysis.
12. Virtual machines: signal processing
with multicore systems.
The approximately 1000 slides are not uni-
formly distributed among the 12 different top-
ics but rather with weighted importance. The
linear signal processing part is concluded in
Chapter 5. From Chapter 6 the neural based
signal processing algorithms are treated fol-
lowed by the use of kilo-processor arrays.
IV.
S
UMMARY
The course material presents an integrated
approach to linear- and nonlinear signal pro-
cessing. it guides the student through the dif-
ferent algorithms and signal descriptions
through time-domain, z-transform FFT, Hop-
field net, Feedforward Neural Networks. It
also elaborates on implementing algorithms on
kilo-processor arrays. In this way, the students
can obtain not only basic concepts but also the
necessary skills to implement thee algorithms
on MATLAB, on TI DSP development kits or
on other platforms.
45
44
45
Basics of Neurobiology
Neurobiology
I.
Zsolt Liposits, Imre Kalló
Pázmány Péter Catholic University
Faculty of Information Technology
Budapest, Hungary
liposits@koki.hu, kallo@koki.hu
Keywords - neurons, glial cells, neurotrans-
mitter and neuromodulator, resting- and action
potential, excitation, inhibition, synapse, nucle-
us, pathway, nerve fiber, forebrain
I.
I
NTRODUCTION
This subject describes the structure and
function of the nervous system by providing
the interested audience with sufficient
knowledge about the cellular elements, major
pathways and the organization rules of the
CNS. The lectures introduce first the neurons
and glial cells and the tissue built from these
elements by explaining their physiology,
chemical composition, membrane processes
and ultrastructure in sufficient details. This is
followed by the demonstration of the major
pathways and functional units of the CNS by
visualizing their location, spatial orientation
and relationship with other units. The tech-
niques employed for the investigation and
demonstration of the structural and functional
characteristics are summarized also in short
presentations.
The formal prerequisite is the subject “Mo-
lecular biology”.
II.
R
ESULTS
This subject presents 37 different topics in
the field of basic neurobiology supplemented
with the demonstrations of methodological
approaches currently used in neuroscience.
Lectures:
Introduction (Quo vadis Neurobiology)
Organ Systems
Organization of the nervous system
The cell
Cell organelles I.-II.
Nervous tissue
The neuron
Nerve fibers
Neuroglia
Nerve endings
Synaptic communication
Neurotransmitters I.-II.
Release of neurotransmitters
Receptors (Ionotropic, Metabotropic)
Neurodegeneration
Development of the nervous system
Spinal cord
Internal structure of spinal cord
Tracts of spinal cord
Stretch reflex
Flexor and autonomic reflexes
Brain stem
Structure of cerebellum
Networking of cerebellum
Organization of the brain stem
Networking of brain stem
Cranial nerves
Diencephalon
Divisions of the Telencephalon
Cytoarchitecture of cerebral cortex
Sensory systems
Motor systems
Hippocampal formation
Olfactory system
Visual system
Cochlear and vestibular systems