CoDi-1Bit : A simplified cellular automata based neuron model
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” . 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)” , 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.
KeywordsCellular Automata Evolutionary Engineering Evolvable Hardware Neural Networks Genetic Algorithms Genetic encoding Artificial Brains Cellular Automata Machine (CAM-8) CAM-Brain Machine (CBM)
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