Course detail

Bio-Inspired Computers

FIT-BINAcad. year: 2022/2023

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.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Learning outcomes of the course unit

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

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

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

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

Mid-term exam, realization and presentation of the project, computer lab assignments in due dates. The minimal number of points which can be obtained from the final exam is 20. Otherwise, no points will be assigned to a student. 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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic 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
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
Miller J.F.: Cartesian Genetic Programming, Springer Verlag, 2011, ISBN 978-3-642-17309-7
Sze V., Chen Y.H., Yang T.J., Emer J.S.: Efficient Processing of Deep Neural Networks. Morgan & Claypool Publishers, 2020, ISBN 978-1681738352
Rozenberg G., Bäck T., Kok J.N.: Handbook of Natural Computing, Springer 2012, 2052 p., ISBN 978-3540929093

Recommended reading

Kvasnička, V., Pospíchal J., Tiňo P.: Evolučné algoritmy. Vydavatelství STU Bratislava, 2000, 215 s., ISBN 80-227-1377-5
Mařík et al.: Umělá inteligence IV, Academia, 2003, 480 s., ISBN 80-200-1044-0

Elearning

Classification of course in study plans

  • Programme IT-MSC-2 Master's

    branch MBI , 1 year of study, summer semester, compulsory
    branch MBS , 0 year of study, summer semester, elective
    branch MGM , 0 year of study, summer semester, elective
    branch MIS , 0 year of study, summer semester, elective
    branch MSK , 0 year of study, summer semester, elective

  • Programme MITAI Master's

    specialization NADE , 0 year of study, summer semester, elective
    specialization NBIO , 0 year of study, summer semester, compulsory
    specialization NCPS , 0 year of study, summer semester, elective
    specialization NEMB , 0 year of study, summer semester, elective
    specialization NGRI , 0 year of study, summer semester, elective
    specialization NHPC , 0 year of study, summer semester, elective
    specialization NIDE , 0 year of study, summer semester, elective
    specialization NISD , 0 year of study, summer semester, elective
    specialization NISY up to 2020/21 , 0 year of study, summer semester, elective
    specialization NMAL , 0 year of study, summer semester, compulsory
    specialization NMAT , 0 year of study, summer semester, elective
    specialization NNET , 0 year of study, summer semester, elective
    specialization NSEC , 0 year of study, summer semester, elective
    specialization NSEN , 0 year of study, summer semester, elective
    specialization NSPE , 0 year of study, summer semester, elective
    specialization NVER , 0 year of study, summer semester, elective
    specialization NVIZ , 0 year of study, summer semester, elective
    specialization NISY , 0 year of study, summer semester, elective

  • Programme IT-MSC-2 Master's

    branch MIN , 0 year of study, summer semester, compulsory-optional
    branch MMM , 0 year of study, summer semester, compulsory-optional
    branch MPV , 0 year of study, summer semester, compulsory-optional

  • Programme MITAI Master's

    specialization NEMB up to 2021/22 , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Syllabus

  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

Exercise in computer lab

8 hod., compulsory

Teacher / Lecturer

Syllabus

  1. Evolutionary design of combinational circuits
  2. Statistical evaluation of experiments with evolutionary design
  3. Celulární automaty
  4. Neuropočítače

Project

18 hod., compulsory

Teacher / Lecturer

Syllabus

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. 

Elearning