%0 Conference Paper %1 deGaris:1994:CAM-BRAIN %A de Garis, Hugo %B Proceedings of the 1994 IEEE World Congress on Computational Intelligence %C Orlando, Florida, USA %D 1994 %I IEEE Press %K algorithms, automata, cellular genetic networks neural %P 337--339b %R doi:10.1109/ICEC.1994.349929 %T CAM-BRAIN The Genetic Programming of an Artificial Brain Which Grows/Evolves at Electronic Speeds in a Cellular Automata Machine %V 1 %X The paper reports on a project which 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 behaviours 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

@inproceedings{deGaris:1994:CAM-BRAIN, abstract = {The paper reports on a project which 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 behaviours 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}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Orlando, Florida, USA}, author = {{de Garis}, Hugo}, biburl = {https://www.bibsonomy.org/bibtex/2d08451f923cc520fcfad63339d4b1b38/brazovayeye}, booktitle = {Proceedings of the 1994 IEEE World Congress on Computational Intelligence}, doi = {doi:10.1109/ICEC.1994.349929}, interhash = {eb1aa7514575c9e63a7095b339448dec}, intrahash = {d08451f923cc520fcfad63339d4b1b38}, keywords = {algorithms, automata, cellular genetic networks neural}, month = {27-29 June}, notes = {It appears growth of cellular automata are controlled by linear fixed length chromosome, ie does not use Koza style tree. The CA grow in channels which convey signals that are isolated from each other except at junctions (synapses). Artificial brain by 2000AD.}, pages = {337--339b}, publisher = {IEEE Press}, size = {6 pages}, timestamp = {2008-06-19T17:38:33.000+0200}, title = {{CAM}-{BRAIN} The Genetic Programming of an Artificial Brain Which Grows/Evolves at Electronic Speeds in a Cellular Automata Machine}, volume = 1, year = 1994 }