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New Generation Computing

, Volume 12, Issue 2, pp 215–221 | Cite as

An artificial brain ATR's CAM-Brain Project aims to build/evolve an artificial brain with a million neural net modules inside a trillion cell Cellular Automata Machine

  • Hugo de Garis
Project Report

Abstract

ATR's Evolutionary Systems Department aims to build (i.e. grow/evolve) an artificial brain by the year 2001. This artificial brain should initially contain thousands of interconnected artificial neural network modules, and be capable of controlling approximately 1000 “behaviors” in a “robot kitten”. The name given to this research project is “CAM-Brain”, because the neural networks (based on cellular automata) will be grown inside special hardware called Cellular Automata Machines (CAMs). Using a family of CAMs, each with its own processor to measure the performance quality or fitness of the evolved neural circuits, will allow the neural modules and their interconnections to be grown/evolved at electronic speeds. State of the art in CAM design is about 10 to the power 9 or 10 cells. Since a neural module of about 15 connected neurons can fit inside a cube of 100 cells on a side (1 million cells), a CAM which is specially adapted for CAM-Brain could contain thousands of interconnected modules, i.e. an artificial brain.

Keywords

CAM-Brain Cellular Automata (CAs) Cellular Automata Machines (CAMs) Artificial Brains Neurite Networks Genetic Programming (GP) Genetic Algorithms (GAs) GenNets (Genetically Programmed Neural Network Modules) CA Networks Artificial Nervous Systems Incremental GP Biots (Biological Robots) Darwinian Robotics 1000-GenNet Biots GenNet Accelerators GenNet Shaping CA Neurons Darwin Machines Nanotechnology NanoCAM-Brain 

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References

  1. 1).
    de Garis, H., “Genetic Programming: Modular Evolution for Darwin Machines”, IJCNN-90-WASH-DC, (Int. Joint Conf. on Neural Networks), January 1990, Washington DC, USA.Google Scholar
  2. 2).
    de Garis, H., “Genetic Programming”, Neural and Intelligent Systems Integration, ed. Soucek B., WILEY, 1991.Google Scholar
  3. 3).
    de Garis, H., “Artificial Embryology: The Genetic Programming of an Artificial Embryo”, Dynamic, Genetic, and Chaotic Programming, Soucek B. and the IRIS Group eds., WILEY, 1992.Google Scholar
  4. 4).
    de Garis, H., “Genetic Programming: GenNets, Artificial Nervous Systems, Artificial Embryos”, WILEY manuscript.Google Scholar
  5. 5).
    Codd, E.F., “Cellular Automata”, Academic Press, 1968.Google Scholar
  6. 6).
    Goldberg, D.E., “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley, 1989.Google Scholar
  7. 7).
    Toffoli, T. & Margolus, N., “Cellular Automata Machines”, MIT Press, 1987.Google Scholar
  8. 8).
    de Garis, H., “Evolvable Hardware: Genetic Programming of a Darwin Machine”, in Artificial Neural Nets and Genetic Algorithms, R.F. Albrecht, C.R. Reeves, N.C. Steele (eds.), Springer-Verlag, 1993.Google Scholar
  9. 9).
    Drexler, K.E., “Nanosystems: Molecular Machinery, Manufacturing and Computation”, Wiley, 1992.Google Scholar

Copyright information

© Ohmsha, Ltd. and Springer 1994

Authors and Affiliations

  1. 1.Evolutionary Systems Dept.ATR Human Information Processing Research LaboratoriesKyotoJapan

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