Module also offered within study programmes:
General information:
Name:
Biocybernetics
Course of study:
2019/2020
Code:
ZSDA-3-0228-s
Faculty of:
Szkoła Doktorska AGH
Study level:
Third-cycle studies
Specialty:
-
Field of study:
Szkoła Doktorska AGH
Semester:
0
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
dr hab. Bielecki Andrzej (bielecki@agh.edu.pl)
Dyscypliny:
Moduł multidyscyplinarny
Module summary

Knowledge of selected biological structures and processes that are or may be the inspiration of algorithms will be the effect of education. Among other things, evolutionary processes, communication in the animal and plant world, and processes in organisms will be discussed. The algorithms based on discussed biological processes and structures as well as their formal and electronic models will be presented. The applications of the discussed algorithms will be considered as well.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Skills: he can
M_U001 The student is able to discuss scientific topics and professionally present the results of his research. SDA3A_U02, SDA3A_U01 Activity during classes
M_U002 The student is able to discuss current scientific results in biocybernetics. SDA3A_U04, SDA3A_U02 Participation in a discussion
Knowledge: he knows and understands
M_W001 The student knows selected biology issues and is able to create their models. He is also able to develop algorithms inspired by biological phenomena. He can search for biological phenomena that can be the basis for solving IT problems. SDA3A_W02, SDA3A_W01 Activity during classes
M_W002 Acquainting with current scientific trends in biocybernetics. SDA3A_W02
M_W003 The student is able to discuss scientific topics related to cybernetics and professionally present research results. SDA3A_W04 Activity during classes
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
15 5 0 0 0 0 10 0 0 0 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Skills
M_U001 The student is able to discuss scientific topics and professionally present the results of his research. - - - - - + - - - - -
M_U002 The student is able to discuss current scientific results in biocybernetics. - - - - - + - - - - -
Knowledge
M_W001 The student knows selected biology issues and is able to create their models. He is also able to develop algorithms inspired by biological phenomena. He can search for biological phenomena that can be the basis for solving IT problems. + - - - - - - - - - -
M_W002 Acquainting with current scientific trends in biocybernetics. + - - - - - - - - - -
M_W003 The student is able to discuss scientific topics related to cybernetics and professionally present research results. - - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 21 h
Module ECTS credits 3 ECTS
Udział w zajęciach dydaktycznych/praktyka 15 h
Preparation for classes 2 h
Realization of independently performed tasks 1 h
Contact hours 1 h
Inne 2 h
Module content
Lectures (5h):

1. Information and signal processing in biological systems.
2. Untypical evolutionary mechanisms.
3. Conditioned reflexes and activity automatization as a basis of AI systems.
4. Molecular automata as the basis of living systems.

Seminar classes (10h):

1. Information and signal processing in biological systems.
2. Untypical evolutionary mechanisms.
3. Conditioned reflexes and activity automatization as a basis of AI systems.
4. Molecular automata as the basis of living systems.

Additional information
Teaching methods and techniques:
  • Lectures: Lectures.
  • Seminar classes: Discussions and presentations (reports).
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

Class attendance, participation in discussions, preparing a presentation.

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Participation in the lectures.
  • Seminar classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Participation in discussion and preparing presentations.
Method of calculating the final grade:

Attendance at all classes is the basis for obtaining a satisfactory grade. If, moreover, the student actively participated in the discussions, he gets a good grade. If, in addition, the student has prepared a presentation, he receives a very good grade.

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Individual consultations.

Prerequisites and additional requirements:

Prerequisites and additional requirements not specified

Recommended literature and teaching resources:

Recommended literature – scientific articles – will be provided by the lecturer at the beginning of the semester in order to take into account the most recent scientific results. Sample topics below.

1. Rohwer F., Barott K. (2013),
Viral information,
Biology and Philosophy, vol.28, 283-297.

2. Trewavas A.J. (1999),
How plants learn.
Proceedings of the National Academy of Science, vol.96, 4216-4218.

3. Jablonka E., Lamb M.J. (2007),
Precis of evolution in four dimensions,
Behavioral and Brain Sciences, vol.30, 353-392.

Scientific publications of module course instructors related to the topic of the module:

1. Bielecki A., Gierdziewicz M., Kalita P. (2019),
A study on efficiency of 3D partial differential diffusive model of presynaptic processes,
Biocybernetics and Biomedical Engineering, accepted.

2. Bielecki A. (2015),
A general entity of life – a cybernetic approach,
Biological Cybernetics, vol.109, 401-419.

3. Bielecki A. (2014)
A model of human activity automatization as a basis of artificial intelligence systems,
IEEE Transactions on Autonomous Mental Development, vol.6, 169-182.

4. Bielecki A., Kalita P. (2012),
Dynamical properties of the reaction-diffusion type model of fast synaptic transport,
Journal of Mathematical Analysis and Applications, vol.393, 329-340.

5. Bielecki A., Kalita P., Lewandowski M., Siwek B. (2010),
Numerical simulation for neurotransmitter transport model in axon terminal of presynaptic neuron,
Biological Cybernetics, vol.102, 489-501.

6. Bielecki A., Kalita P., Lewandowski M., Skomorowski M. (2008),
Compartment model of neuropeptide synaptic transport with impulse control,
Biological Cybernetics, vol.99, 443-458.

7. Bielecki A., Kalita P. (2008),
Model of neurotransmitter fast transport in axon terminal of presynaptic neuron,
Journal of Mathematical Biology, vol.56, 559-576.

8. Kokoszka A., Bielecki A., Holas P. (2001),
Mental organization according to metabolism of information and its mathematical description,
International Journal of Neuroscience, vol.107, 173-184.

9. Bielecki A., Kokoszka A., Holas P. (2000),
Dynamic systems theory approach to consciousness,
International Journal of Neuroscience, vol.104, 29-47.

Additional information:

None