CoDi-1Bit : A simplified cellular automata based neuron model

  • Felix Gers
  • Hugo de Garis
  • Michael Korkin
Evolvable Hardware and Robotics
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1363)


This paper presents some simplifications to our recently introduced “CoDi-model”, which we use to evolve Cellular Automata based neural network modules for ATR's artificial brain project “CAM-Brain” [11]. The great advantage of CAs as a modeling medium, is their parallelism, which permits neural system simulation hardware based on CoDi to be scaled up without loss of speed. Simulation speed is crucial for systems using ldevolutionary engineering” technologies, such as ATR's CAM-Brain Project, which aims to build/grow/evolve a billion neuron artificial brain. The improvements in the CoDi model simplify it sufficiently, so that it can be implemented in state of the art FPGAs (e.g. Xilinx's XC6264 chips). ATR is building an FPGA based Cellular Automata Machine “CAM-Brain Machine (CBM)” [13], which includes circuits for neural module evolution and will simulate CoDi about 500 times faster than MIT's Cellular Automata Machine CAM-8 currently used at ATR.


Cellular Automata Evolutionary Engineering Evolvable Hardware Neural Networks Genetic Algorithms Genetic encoding Artificial Brains Cellular Automata Machine (CAM-8) CAM-Brain Machine (CBM) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Toffoli, T. & Margolous, N. ‘Cellular Automata Machines”, MIT Press, Cambridge, MA, 1987.Google Scholar
  2. 2.
    von Neumann, J. ‘Theory of Self-Reproducing Automata', ed. Burks A.W. University of Illinois Press, Urbana, 1966Google Scholar
  3. 3.
    Codd, E.F. ‘Cellular Automata', Academic Press, NY 1968.Google Scholar
  4. 4.
    de Garis, H. ‘CAM-BRAIN: The Evolutionary Engineering of a Billion Neuron Artificial Brain by 2001 which Grows/Evolves at Electronic Speed Inside a Cellular Automata Machine (CAM)', in ‘Towards Evolvable Hardware', Springer, Berlin, Heidelberg, NY, 1996.Google Scholar
  5. 5.
    Lloyd, S. ‘A Potentially Realizable Quantum Computer', Science 261, 1569–1571 1993.Google Scholar
  6. 6.
    Koza J.R. ‘Genetic Programming: On the Programming of Computers by the Means of Natural Selection', Cambrige, MA, MIT Press, 1992Google Scholar
  7. 7.
    Koza, J.R. & Bennet, F.H. & Andre, D. & Keane, M.M. ‘Toward Evolution of Electronic Animals Using Genetic Programming', ALife V Conference Proceedings, MIT Press, 1996.Google Scholar
  8. 8.
    Sipper, M. ‘Co-evolving Non-Uniform Cellular Automata to Perform Computations'; Physica D 92, 193–208 1996.Google Scholar
  9. 9.
    Carter, F. L. ‘Molecular Electronic Devices', North-Holland, Amsterdam, NY, Oxford. Tokyo, 1986.Google Scholar
  10. 10.
    Gers, F. A. & de Garis, H. ‘Porting a Cellular Automata Based Artificial Brain to MIT's Cellular Automata Machine 'CAM-8', SEAL'96 Conference Proceedings S7-3, 1996.Google Scholar
  11. 11.
    Gers, F. A. & de Garis, H. ‘CAM-Brain: A New Model for ATR's Cellular Automata based Artificial Brain Project', ICES'96 Conference Proceedings S7-5, 1996. 12. Margolus, N. 'Crystalline Computation', (Preprint).Google Scholar
  12. 13.
    Korkin M. & de Garis H. &, Gers F.A. & Hemmi H. ‘CBM (CAM-Brain Machine): A Hardware Tool which Evolves a Neural Net Module in a Fraction of a Second and Runs a Million Neuron Artificial Brain in Real Time', Genetic Programming Conference, July 1997, Stanford, USA, 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Felix Gers
    • 1
  • Hugo de Garis
    • 1
  • Michael Korkin
    • 2
  1. 1.ATR, Human Information Processing LaboratoriesSoraku-gun, KyotoJapan
  2. 2.Genobyte Inc.BoulderUSA

Personalised recommendations