Course Name 
Neurocomuting
Course Overview 
Level of study	Master Degree 	
Workload	ECTS: 		3
Total Hours:	108
Contact Hours:	48
•	Lectures:	16
•	Labs:		16
•	Seminars:	16	
Course Code	М2.В.2.3	
Semester	Fall	
Prerequisites	Informatics, Programming, Probability theory	
Course Objectives	The main goal - to prepare students for research and design activity.	
Learning Outcomes	To give systematic overview of modern models of biologic
and artificial neural networks, to study techniques
for their application in information processing and image
recognition.
By the end of the course the students will know:
modern models of biologic and artificial neural networks,
application techniques for information processing
and image recognition.
The students will be able to:
set problems and develop algorithms for their solution to
to develop software implementation of neural networks
for processing statistic and video images.
The students will have the experience in:
application technologies for mathematical methods and
in neural network processing of large volumes of space time
data.	
Syllabus	Topic 1 Biologic and artificial neural networks (lectures
2)
Topic 2 One-layered and multi-layered perceptrons (lectures
2, labs 2)
Topic 3 Networks on the basis of radial basis function
(lectures2, labs 4)
Topic 4 Support vectors machines (lectures 4, labs 2)
Topic 5 Analysis of key components (lectures 2, labs 4)
Topic 6 Kohonen's self-organisation maps (lectures 2,
labs 2)
Topic 7 Biologically probable models of neural networks
(lectures 2, labs 2)	
Labs	Lab 1 Classification tasks 
Lab 2 Approximation tasks 
Lab 3 Hybrid models 
Lab 4 Project (Team Work)	
Assessment	Credit test	
Resources	Compulsory
1. S. Khaikin. 2006. Neural networks: full course. Moscow.:
Williams, 1104p.
2. S. Osovsky. 2007. Neural networks for information
processing. Moscow.: Finansy i statistika, 345p.
3. V. G. Spitsyn, Y. R. Tsoy. 2008. Knowledge representation
in information systems: handbook. Tomsk:
TPU publishing, 152p.
4. Arbib M. A. 2003. The Handbook of Brain Theory
and Neural Networks Oxford Cambridge, Massachusetts,
USA: MIT Press Inc., 1301p.
Further reading
1. Bishop C. M. 2005. Neural Networks for Pattern
Recognition. New York, USA: Oxford University Press
Inc., 251p.
2. Acharya T., Ray A. K. 2005. Image Processing. Principles
and Applications, Hoboken, New Jersey, USA:
John Wiley & Sons, Inc., 451p.
3. Duda R. O., Hart P. E., Stork D. G. 2001. Pattern
Classification. USA: John Wiley & Sons, Inc., 654p.
4. Y. B. Kazanovich. 2010. Theory of time correlation
and models of segmentation of visual information in brain (overview) // Мmathematical biology and bioinformatics.
v. 5. # 1, pp. 43-97.
Software and Internet resources
1. Software for laboratory works in Visual Studio 2010.
http://raai.org/ – Russian association of neural networks.
2. http://www.niisi.ru/iont/ni – Russian association of
neuroinformatics.
3. EJ «Neuroinformatics».
http://www.niisi.ru/iont/ni/Journal/.
4. Neural Computation Journal.
http://www.mitpressjournals.org/loi/neco.
5. http://www.elsevier.com/wps/find/journaldescription.
cws_home/505628/description
6. The Journal of Neuroscience.
http://www.jneurosci.org/
7. EJ «Pattern Recognition and Image Analysis»
http://www.maik.ru/cgiperl/
1.	journal.pl?name=patrec&page=main	
Instructors	Axyonov Sergey Vladimirovich
Assistant Professor
Optimization System Control Department
Institute of Cybernetics
http://portal.tpu.ru/SHARED/a/axoenowsw/eng