Molekuláris bionika és infobionika szakok tananyagának komplex



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

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45

Basics of Neurobiology 

Neurobiology

 I. 

Zsolt Liposits, Imre Kalló  

Pázmány Péter Catholic University 

Faculty of Information Technology 

Budapest, Hungary 

[e-mail], [e-mail] 

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 

---