Title:

Bio-Inspired Computers

Code:BIN
Ac.Year:2019/2020
Sem:Summer
Curriculums:
ProgrammeField/
Specialization
YearDuty
IT-MSC-2MBI1stCompulsory
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Elective
IT-MSC-2MIN-Compulsory-Elective - group I
IT-MSC-2MIS-Elective
IT-MSC-2MMM-Compulsory-Elective - group N
IT-MSC-2MPV-Compulsory-Elective - group B
IT-MSC-2MSK-Elective
MITAINADE-Elective
MITAINBIO-Compulsory
MITAINCPS-Elective
MITAINEMB-Elective
MITAINGRI-Elective
MITAINHPC-Elective
MITAINIDE-Elective
MITAINISD-Elective
MITAINISY-Elective
MITAINMAL-Compulsory
MITAINMAT-Elective
MITAINNET-Elective
MITAINSEC-Elective
MITAINSEN-Elective
MITAINSPE-Elective
MITAINVER-Elective
MITAINVIZ-Elective
Language of Instruction:Czech
Private info:http://www.fit.vutbr.cz/study/courses/BIN/private/
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2600818
 ExamsTestsExercisesLaboratoriesOther
Points:52150825
Guarantor:Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Deputy guarantor:Bidlo Michal, Ing., Ph.D. (DCSY)
Lecturer:Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Instructor:Bidlo Michal, Ing., Ph.D. (DCSY)
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY)
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Systems FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.Groups
WedlecturelecturesA112 14:0015:501MIT 2MIT MBI xx
 
Learning objectives:
  To understand the principles of bio-inspired computational systems. To be able to use the bio-inspired techniques in the design, implementation and operational phases of a computational system.
Description:
  This course introduces computational models and computers which have appeared at the intersection of hardware and artificial intelligence in the recent years as an attempt to solve computational and energy inefficiency of conventional computers. The course surveys relevant theoretical models, reconfigurable architectures and computational intelligence techniques inspired at the levels of phylogeny, ontogeny and epigenesis. In particular, the following topics will be discussed: emergence and self-organization, evolutionary design, evolvable hardware, cellular systems, neural hardware, molecular computers and nanotechnology. Typical applications will illustrate the mentioned approaches.
Subject specific learning outcomes and competencies:
  Students will be able to utilize evolutionary algorithms to design computational structures and electronic circuits. They will be able to model, simulate and implement non-conventional, in particular bio-inspired, computational systems.
Generic learning outcomes and competencies:
  Understanding the relation between computers (computing) and some natural processes.
Why is the course taught:
  Many phenomena observed in nature (such as evolution, self-organization and learning) can be understood as computational processes. Inspired in these phenomena, you will learn how to design algorithms and computers showing properties (such as adaptation, self-organization, energy efficiency) that are hard to achieve by means of conventional techniques developed in computer science and engineering.
Syllabus of lectures:
 
  1. Introduction, inspiration in biology, entropy and self-organization
  2. Limits of abstract and physical computing
  3. Evolutionary design
  4. Cartesian genetic programming
  5. Reconfigurable computing devices
  6. Evolutionary design of electronic circuits
  7. Evolvable hardware, applications
  8. Computational development
  9. Neural networks and neuroevolution
  10. Neural hardware
  11. DNA computing
  12. Nanotechnology and molecular electronics
  13. Recent trends
Syllabus of computer exercises:
 
  1. Evolutionary design of combinational circuits
  2. Statistical evaluation of experiments with evolutionary design
  3. Virtual reconfigurable circuits
  4. Celulární automaty
Syllabus - others, projects and individual work of students:
 Every student will choose one project from a list of approved projects that are relevant for this course. The implementation, presentation and documentation of the project will be evaluated. 
Fundamental literature:
 
  • Sekanina L., Vašíček Z., Růžička R., Bidlo M., Jaroš J., Švenda P.: Evoluční hardware: Od automatického generování patentovatelných invencí k sebemodifikujícím se strojům. Academia Praha 2009, ISBN 978-80-200-1729-1. (in Czech)
  • Floreano, D., Mattiussi, C.: Bioinspired Artificial Intelligence: Theories, Methods, and Technologies. The MIT Press, Cambridge 2008, ISBN 978-0-262-06271-8.
  • Trefzer M., Tyrrell A.M.: Evolvable Hardware - From Practice to Application. Berlin: Springer Verlag, 2015, ISBN 978-3-662-44615-7.
  • Rozenberg G., Bäck T., Kok J.N.: Handbook of Natural Computing, Springer 2012, 2052 p., ISBN 978-3540929093Miller J.F.: Cartesian Genetic Programming, Springer Verlag 2011, ISBN 978-3-642-17309-7.
Study literature:
 
  • Sekanina L., Vašíček Z., Růžička R., Bidlo M., Jaroš J., Švenda P.: Evoluční hardware: Od automatického generování patentovatelných invencí k sebemodifikujícím se strojům. Academia Praha 2009, ISBN 978-80-200-1729-1. (in Czech)
  • Floreano, D., Mattiussi, C.: Bioinspired Artificial Intelligence: Theories, Methods, and Technologies. The MIT Press, Cambridge 2008, ISBN 978-0-262-06271-8.
  • Trefzer M., Tyrrell A.M.: Evolvable Hardware - From Practice to Application. Berlin: Springer Verlag, 2015, ISBN 978-3-662-44615-7
  • Rozenberg G., Bäck T., Kok J.N.: Handbook of Natural Computing, Springer 2012, 2052 p., ISBN 978-3540929093.
  • Kvasnička, V., Pospíchal J., Tiňo P.: Evolučné algoritmy. Vydavatelství STU Bratislava, 2000, 215 s., ISBN 80-227-1377-5. (in Czech)
  • Mařík et al.: Umělá inteligence IV, Academia, 2003, 480 s., ISBN 80-200-1044-0. (in Czech).
Controlled instruction:
  Mid-term exam, realization and presentation of the project, computer lab assignments in due dates. In the case of a reported barrier preventing the student to defend the project or solve a lab assignment, the student will be allowed to defend the project or solve the lab assignment on an alternative date.
Progress assessment:
  Mid-term exam, project and its presentation, computer lab assignments. 
Exam prerequisites:
  None
 

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