Faculty of Information Technology, BUT

Course details

Bio-Inspired Computers

BIN Acad. year 2019/2020 Summer semester 5 credits

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.

Guarantor

Deputy Guarantor

Bidlo Michal, Ing., Ph.D. (DCSY FIT BUT)

Language of instruction

Czech

Completion

Examination (written)

Time span

26 hrs lectures, 8 hrs pc labs, 18 hrs projects

Assessment points

52 exam, 15 half-term test, 8 labs, 25 projects

Department

Lecturer

Instructor

Course Web Pages

Subject specific learning outcomes and competences

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 competences

Understanding the relation between computers (computing) and some natural processes.

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.

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.

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).

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.

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 - 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. 

Progress assessment

Mid-term exam, project and its presentation, computer lab assignments. 

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.

Exam prerequisites

None

Schedule

DayTypeWeeksRoomStartEndLect.grpGroupsInfo
Wedlecturelectures A112 14:0015:50 1MIT 2MIT MBI xx

Course inclusion in study plans

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