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Adaptation, Noise, and Self-Organizing Systems papers — page 13

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Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error
Neural network models offer a theoretical testbed for the study of learning at the cellular level. The only experimentally verified learning rule, Hebb's rule, is extremely limited in its ability…
Russell W. AndersonJun 2, 1993
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A Criterion for Stability in Random Boolean Cellular Automata
Random boolean cellular automata are investigated, where each gate has two randomly chosen inputs and is randomly assigned a boolean function of its inputs. The effect of non-uniform distributions on…
James F. LynchMay 11, 1993
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Antichaos in a Class of Random Boolean Cellular Automata
A variant of Kauffman's model of cellular metabolism is presented. It is a randomly generated network of boolean gates, identical to Kauffman's except for a small bias in favor of boolean…
James F. LynchApr 26, 1993
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Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations
We present results from an experiment similar to one performed by Packard (1988), in which a genetic algorithm is used to evolve cellular automata (CA) to perform a particular computational task.…
Melanie Mitchell, Peter Hraber, James P. CrutchfieldMar 31, 1993
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Evolution to the Edge of Chaos in Imitation Game
Motivated by the evolution of complex bird songs, an abstract imitation game is proposed to study the increase of dynamical complexity: Artificial "birds" display a "song" time series…
Kunihiko Kaneko, Junji SuzukiMar 8, 1993
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The length of a typical Huffman codeword
If p is the probability of a letter of a memoryless source, the length l of the corresponding binary Huffman codeword can be very different from the value -log p. We show that, nevertheless, for a…
R. SchackMar 5, 1993
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