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Computation at the edge of chaos: Phase transitions and emergent computation
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“As it has been proposed, adaptive systems require a high C in order to be able to cope with changes of its environment while at the same time maintaining their integrity (Langton, 1990; Kauffman, 1993).”
From Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis (2013) · cites this paper as Langton, 1990
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“As it has been proposed by several authors, complexity can be seen as balance between order and disorder (Langton, 1990; Kauffman, 1993; Lopez-Ruiz et al., 1995)”
From Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis (2013) · cites this paper as Langton, 1990
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“It has been argued that computation and life require this balance to be able to compute and adapt (Langton, 1990; Kauffman, 1993).”
From Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis (2013) · cites this paper as Langton, 1990
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“Finally, we show that a randomly connected circuit model, akin to a “reservoir” network13-15, can reproduce the observed distribution of timescales, provided that the network operates at the critical point (or “edge of chaos”)16-18.”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [18]
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Cites
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Theory of Self-Reproducing Automata
Schwartz, J. T., von Neumann, J., & Burks, A. W. (1967) · Mathematics of Computation · doi:10.2307/2005041 · Cited by 5,468
Metabolic stability and epigenesis in randomly constructed genetic nets
Kauffman, S. A. (1969) · Journal of Theoretical Biology · doi:10.1016/0022-5193(69)90015-0 · Cited by 4,871
Bak, P., Tang, C., & Wiesenfeld, K. A. (1988) · Physical Review A · doi:10.1103/physreva.38.364 · Cited by 4,744
Statistical mechanics of cellular automata
Wolfram, S. (1983) · Reviews of Modern Physics · doi:10.1103/revmodphys.55.601 · Cited by 3,199
Universality and complexity in cellular automata
Wolfram, S. (1984) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(84)90245-8 · Cited by 1,956
Fredkin, E., & Toffoli, T. (1982) · International Journal of Theoretical Physics · doi:10.1007/bf01857727 · Cited by 1,896
Gardner, M. R. (1970) · Scientific American · doi:10.1038/scientificamerican1070-120 · Cited by 1,764
Complexity, Entropy And The Physics Of Information
Zurek, W. H. (2018) · CRC Press eBooks · doi:10.1201/9780429502880 · Cited by 1,451
Winning Ways for Your Mathematical Plays.
Connelly, R., Berlekamp, E. R., Conway, J. H., & Guy, R. K. (1986) · American Mathematical Monthly · doi:10.2307/2323620 · Cited by 1,432
Research works printed but not linked: 1
- Kolmogorov, 1965, Prob. Inf. Transm., 1, 1
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Sorted by citations
Items 801–900 of 1,002
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Groß, T., & Do, A.-L. (2022) · River Publishers eBooks · doi:10.1201/9781003339458 · Cited by 5
Weinert, W. R., & Lopes, H. S. (2009) · Biosystems · doi:10.1016/j.biosystems.2009.08.002 · Cited by 5
Information dynamics at the edge of chaos: Measures, examples, and principles
Prokopenko, M. (2013) · IEEE Symposium on Artificial Life (ALIFE) · doi:10.1109/alife.2013.6602443 · Cited by 6
Sapin, E., Adamatzky, A., Collet, P., & Bull, L. (2009) · Natural Computing · doi:10.1007/s11047-009-9109-0 · Cited by 5
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Henz, M., & Misra, J. (2007) · IEEE Symposium on Artificial Life (ALIFE) · doi:10.1109/alife.2007.367654 · Cited by 5
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White, R., & Engelen, G. (1993) · Springer eBooks · doi:10.1007/978-3-642-78463-7_10 · Cited by 5
Scientometrics and the evaluation of European integration
Frenken, K., & Loet Leydesdorff, A. (2009) · TU/e Research Portal · doi:10.48550/arxiv.0912.1231 · Cited by 5
A model of the neural substrates for exploratory dynamics in basal ganglia
Chakravarthy, V. S. (2013) · Progress in brain research · doi:10.1016/b978-0-444-62604-2.00020-4 · Cited by 5
Dynamics, Complexity and Computation
Dufort, P. A., & Lumsden, C. J. (1997) · Studies of nonlinear phenomena in life science · doi:10.1142/9789812819598_0004 · Cited by 5
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Williams, S., & Yaeger, L. (2017) · Geosciences · doi:10.3390/geosciences7030049 · Cited by 5
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Zhang, G., Zhang, C., Li, Z., & Zhang, W. (2018) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2018.8665099 · Cited by 5
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Lizier, J. T. (2012) · Springer theses · doi:10.1007/978-3-642-32952-4_7 · Cited by 5
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Hubler, A. W., & Wotherspoon, T. (2008) · Complexity · doi:10.1002/cplx.20272 · Cited by 5
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Ninagawa, S. (2005) · Lecture notes in computer science · doi:10.1007/11553090_46 · Cited by 5
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Rybarsch, M., & Bornholdt, S. (2014) · Wiley eBooks · doi:10.1002/9783527651009.ch10 · Cited by 5
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Dale, R. A. C. (2024) · doi:10.21428/e2759450.fe6e37e6 · Cited by 5
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Baetens, J. M., & Bernard De Baets, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11520-7_3 · Cited by 4
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Moriya, S., Yamamoto, H., Hirano‐Iwata, A., Kubota, S., & Sato, S. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852207 · Cited by 4
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Ogasawara, Y., & Oishi, S. (2013) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.83.014001 · Cited by 4
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Lee, T., & Tramontano, G. (2021) · World Journal of Neuroscience · doi:10.4236/wjns.2021.112014 · Cited by 3
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Rajagopalan, R. (2020) · Contemporary systems thinking · doi:10.1007/978-3-030-49135-2_2 · Cited by 4
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Vassilev, V. K., Miller, J. F., & Fogarty, T. C. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48304-7_23 · Cited by 4
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Morita, M. (1995) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00040310 · Cited by 4
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Honma, N., KITAGAWA, K., & Abe, K. (1998) · Artificial Life and Robotics · doi:10.1007/bf02471163 · Cited by 4
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Chown, E. (1995) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0004019x · Cited by 4
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Gómez, V. J., Kaltenbrunner, A., López, V., & Kappen, H. J. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0808.3129 · Cited by 4
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Zhang, L., Feng, L., Chen, K., & Lai, C. H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.09437 · Cited by 13
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Vidiella, B., Guillamon, A., Josep Sardanyés, A., Maull, V., Pueyo, N. C., & Solé, R. V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.16.385385 · Cited by 4
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Suetani, H., & Parlitz, U. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0283017 · Cited by 3
Seeking optimal parameters for achieving a lightweight reservoir computing: A computational endeavor
Zhao, B. (2022) · Electronic Research Archive · doi:10.3934/era.2022152 · Cited by 3
Towards a Framework for Unifying Research in Theoretical Psychology
Medintsev, V. A. (2023) · Integrative Psychological and Behavioral Science · doi:10.1007/s12124-023-09790-z · Cited by 3
Shaping dynamical neural computations using spatiotemporal constraints
Kim, J. Z., Larsen, B., & Parkes, L. (2024) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2024.150302 · Cited by 3
Controllable gliders in a nanomagnetic metamaterial
Penty, A., Jensen, J. H., Breivik, I., Strømberg, A., Folven, E., & Tufte, G. (2025) · Nature Communications · doi:10.1038/s41467-025-62515-1 · Cited by 3
Synaptic plasticity alters the nature of the chaos transition in neural networks
Du, W., & Huang, H. (2025) · Physical review. E · doi:10.1103/7kk9-3jm8 · Cited by 3
A molecular treatment of successful destinations: revisiting methodological individualism
Loong, B. L. S. (2012) · European Journal of Tourism Research · doi:10.54055/ejtr.v5i2.100 · Cited by 3
Self-Organization and Emergence of Dynamic Systems
Gimzewski, J. K., Stieg, A. Z., & Vesna, V. (2015) · Springer eBooks · doi:10.1007/978-3-319-04033-2_74-1 · Cited by 3
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Horio, Y., Hiroyasu, A. N. D. O., & Aihara, K. (2009) · IEICE ESS FUNDAMENTALS REVIEW · doi:10.1587/essfr.3.2_34 · Cited by 3
Adaptive Local Information Transfer in Random Boolean Networks
Haruna, T. (2017) · Artificial Life · doi:10.1162/artl_a_00224 · Cited by 3
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Metadata
- Authors
- Langton, C. G.
- Affiliations
- Los Alamos National Laboratory (US)
- Corresponding author
- Langton, Chris G.
- Venue
- Physica D: Nonlinear Phenomena
- Publisher
- Elsevier BV
- Published
- 1990-06
- Volume
- 42
- Issue
- 1-3
- Pages
- 12-37
- ISSN
- 0167-2789 (ISSN-L)
- OpenAlex
- W2177218873
- Cited by
- 1,527
- References
- 26
- Citations by year
- 2026: 66 · 2025: 61 · 2024: 93 · 2023: 51 · 2022: 67 · 2021: 63 · 2020: 71 · 2019: 48 · 2018: 73 · 2017: 70 · 2016: 42 · 2015: 56 · 2014: 51 · 2013: 72 · 2012: 59
- FWCI
- 14.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Physica D Nonlinear Phenomena (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Cellular Automata and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Systems and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Keywords
- emergent computation, phase transitions, cellular automata, tipping points, second-order phase transition, information transmission, information storage, complexity classes, halting problem, edge of chaos
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