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Related Experiment Videos

The Embryonics Project: a machine made of artificial cells.

G Tempesti1, D Mange, A Stauffer

  • 1Logic Systems Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland.

Rivista Di Biologia
|June 4, 1999
PubMed
Summary

The Embryonics Project designs artificial cells inspired by biological growth. These self-replicating and self-repairing processors form massively parallel arrays for complex systems.

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Area of Science:

  • Biologically-inspired computing
  • Developmental electronics
  • Artificial life systems

Background:

  • The concept of bio-inspired computing dates back to John von Neumann's work in the 1940s on self-replicating machines.
  • Early electronic technology limited the physical realization of von Neumann's bio-inspired designs.
  • Advancements in programmable circuits in the 1980s revitalized the field of bio-inspired machines.

Purpose of the Study:

  • To introduce the Embryonics (embryonic electronics) Project.
  • To leverage ontogenetic processes from multicellular organisms for designing novel massively parallel processor arrays.
  • To create 'artificial cells' that mimic biological growth and function.

Main Methods:

  • Designing simple processors ('artificial cells') with identical hardware and an 'artificial genome'.

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  • Implementing differential genome execution based on spatial coordinates within the array.
  • Utilizing programmable elements ('artificial molecules') for adaptable cell structures.

    • Main Results:

    • Achieved self-replication and self-repair (cicatrization) capabilities within the artificial cells, mirroring biological organisms.
    • Enabled adaptable cell structures through programmable elements.
    • Demonstrated the potential for creating complex systems through parallel operation of simple processors.

    Conclusions:

    • The Embryonics Project successfully applies biological ontogeny to create advanced artificial cells.
    • The developed artificial cells exhibit self-replication and self-repair, crucial for robust computing systems.
    • This approach paves the way for highly complex, multicellular-like artificial systems.