Course details

Bio-Inspired Computers

BIN Acad. year 2022/2023 Summer semester 5 credits

Current academic year

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.


Course coordinator

Language of instruction



Examination (written)

Time span

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

Assessment points

  • 52 pts final exam (written part)
  • 15 pts mid-term test (written part)
  • 8 pts labs
  • 25 pts projects




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.
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

  • 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

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
  • 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

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. Celulární automaty
  4. Neuropočítače

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. 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.

Exam prerequisites


Course inclusion in study plans

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