TY - GEN
T1 - Polynomial cellular neural networks for implementing the game of life
AU - Pazienza, Giovanni Egidio
AU - Gomez-Ramirez, Eduardo
AU - Vilasís-Cardona, Xavier
PY - 2007
Y1 - 2007
N2 - One-layer space-invariant Cellular Neural Networks (CNNs) are widely appreciated for their simplicity and versatility; however, such structures are not able to solve non-linearly separable problems. In this paper we show that a polynomial CNN - that has with a direct VLSI implementation - is capable of dealing with the 'Game of Life', a Cellular Automaton with the same computational complexity as a Turing machine. Furthermore, we describe a simple design algorithm that allows to convert the rules of a Cellular Automaton into the weights of a polynomial CNN.
AB - One-layer space-invariant Cellular Neural Networks (CNNs) are widely appreciated for their simplicity and versatility; however, such structures are not able to solve non-linearly separable problems. In this paper we show that a polynomial CNN - that has with a direct VLSI implementation - is capable of dealing with the 'Game of Life', a Cellular Automaton with the same computational complexity as a Turing machine. Furthermore, we describe a simple design algorithm that allows to convert the rules of a Cellular Automaton into the weights of a polynomial CNN.
UR - https://www.scopus.com/pages/publications/38149100142
U2 - 10.1007/978-3-540-74690-4_93
DO - 10.1007/978-3-540-74690-4_93
M3 - Conference contribution
AN - SCOPUS:38149100142
SN - 9783540746898
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 914
EP - 923
BT - Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings
PB - Springer Verlag
T2 - 17th International Conference on Artificial Neural Networks, ICANN 2007
Y2 - 9 September 2007 through 13 September 2007
ER -