Breaking of the Trade-Off Principle between Computational Universality and Efficiency by Asynchronous Updating

Although natural and bioinspired computing has developed significantly, the relationship between the computational universality and efficiency beyond the Turing machine has not been studied in detail. Here, we investigate how asynchronous updating can contribute to the universal and efficient computation in cellular automata (CA). First, we define the computational universality and efficiency in CA and show that there is a trade-off relation between the universality and efficiency in CA implemented in synchronous updating. Second, we introduce asynchronous updating in CA and show that asynchronous updating can break the trade-off found in synchronous updating. Our finding spells out the significance of asynchronous updating or the timing of computation in robust and efficient computation.

[1]  P. Bak,et al.  Earthquakes as a self‐organized critical phenomenon , 1989 .

[2]  Nils Bertschinger,et al.  Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks , 2004, Neural Computation.

[3]  Andrew Schumann,et al.  PHYSARUM SPATIAL LOGIC , 2011 .

[4]  Bak,et al.  Punctuated equilibrium and criticality in a simple model of evolution. , 1993, Physical review letters.

[5]  Artiom Alhazov,et al.  Membrane Computing , 2013, Lecture Notes in Computer Science.

[6]  Gheorghe Paun,et al.  DNA Computing: New Computing Paradigms , 1998 .

[7]  P. Venail,et al.  Experimental niche evolution alters the strength of the diversity–productivity relationship , 2011, Nature.

[8]  Computational capabilities at the edge of chaos for one dimensional systems undergoing continuous transitions. , 2019, Chaos.

[9]  Martin Kutrib Efficient Universal Pushdown Cellular Automata and Their Application to Complexity , 2001, MCU.

[10]  The Evolution of Resource Adaptation: How Generalist and Specialist Consumers Evolve , 2006, Bulletin of mathematical biology.

[11]  Carlos Garcia Cordero Parameter Adaptation and Criticality in Particle Swarm Optimization , 2017, ArXiv.

[12]  R. Kassen The experimental evolution of specialists, generalists, and the maintenance of diversity , 2002 .

[13]  J. Michael Herrmann,et al.  CriPS: Critical Particle Swarm Optimisation , 2015, ECAL.

[14]  Yukio-Pegio Gunji Self-Organized Criticality in Asynchronously Tuned Elementary Cellular Automata , 2014, Complex Syst..

[15]  Hildegard Meyer-Ortmanns,et al.  Phase Transition between Synchronous and Asynchronous Updating Algorithms , 2007 .

[16]  H. Blok,et al.  Synchronous versus asynchronous updating in the ''game of Life'' , 1999 .

[17]  S. Wolfram Statistical mechanics of cellular automata , 1983 .

[18]  B. Schönfisch,et al.  Synchronous and asynchronous updating in cellular automata. , 1999, Bio Systems.

[19]  V. Barbu Self‐organized criticality of cellular automata model; absorbtion in finite‐time of supercritical region into the critical one , 2013 .

[20]  Mikhail Prokopenko,et al.  Self-referential basis of undecidable dynamics: from The Liar Paradox and The Halting Problem to The Edge of Chaos , 2017, Physics of life reviews.

[21]  Matthew Cook,et al.  Universality in Elementary Cellular Automata , 2004, Complex Syst..

[22]  Nazim Fatès,et al.  Fully asynchronous behavior of double-quiescent elementary cellular automata , 2006, Theor. Comput. Sci..

[23]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[24]  Thomas G. Dietterich Overfitting and undercomputing in machine learning , 1995, CSUR.

[25]  Michael Conrad,et al.  On design principles for a molecular computer , 1985, CACM.

[26]  Kenichi Morita,et al.  Reversible Simulation of One-Dimensional Irreversible Cellular Automata , 1995, Theor. Comput. Sci..

[27]  Andrew Adamatzky,et al.  Physarum Machines: Computers from Slime Mould , 2010 .

[28]  Leon O. Chua,et al.  Neurons are Poised Near the Edge of Chaos , 2012, Int. J. Bifurc. Chaos.

[29]  Sukanta Das,et al.  Asynchronous cellular automata and pattern classification , 2015, Complex..

[30]  N. Margolus Physics-like models of computation☆ , 1984 .

[31]  Yuxia Li,et al.  Global dissipativity and quasi-synchronization of asynchronous updating fractional-order memristor-based neural networks via interval matrix method , 2018, J. Frankl. Inst..

[32]  Nazim Fatès,et al.  An Experimental Study of Robustness to Asynchronism for Elementary Cellular Automata , 2004, Complex Syst..

[33]  Masashi Aono,et al.  Robust and emergent Physarum logical-computing. , 2004, Bio Systems.

[34]  Nazim Fatès,et al.  A Guided Tour of Asynchronous Cellular Automata , 2013, J. Cell. Autom..

[35]  S. Kauffman,et al.  Coevolution to the edge of chaos: coupled fitness landscapes, poised states, and coevolutionary avalanches. , 1991, Journal of theoretical biology.

[36]  Stephen Wolfram,et al.  A New Kind of Science , 2003, Artificial Life.

[37]  Stephen Wolfram,et al.  Universality and complexity in cellular automata , 1983 .

[38]  Nazim Fatès Asynchronous cellular automata , 2018 .

[39]  Yukio Gunji Extended self organised criticality in asynchronously tuned cellular automata , 2014 .

[40]  Andrew Adamatzky,et al.  Collision-Based Computing , 2002, Springer London.

[41]  Jackie E. Shay,et al.  Evolution of Ecological Niche Breadth , 2017 .

[42]  Christopher G. Langton,et al.  Computation at the edge of chaos: Phase transitions and emergent computation , 1990 .