Spontaneous evolution of modularity and network motifs.

By Nadav Kashtan, Uri Alon. Proceedings of the National Academy of Sciences of the United States of America, 2005

905 citations recorded by the source corpus.

Subfields: Molecular Biology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/93a420b4c39909b5ffd78fc156737012099f8aa0

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Citations in the corpus

Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits (2010, background): “The hypothesis that biological and artificial information processing systems, in particular, might share network properties such as hierarchical modularity that confer adaptivity or evolvability of function [8] dates back to Simon’s prescient analysis [9] but has not yet been extensively tested usin”
Life is Physics: Evolution as a Collective Phenomenon Far From Equilibrium (2010, background): “In the first, the network was evolved in an environment of goals that changed in a modular fashion[112]; moreover, a follow-up study[113] showed clearly that environmental fluctuations do indeed accelerate the rate of evolution.”
Disrupted Modularity and Local Connectivity of Brain Functional Networks in Childhood-Onset Schizophrenia (2010, positive): “Modularity may allow the brain to adapt to multiple, distinct selection criteria over time (Kashtan and Alon, 2005).”
Modular Brain Networks (2015, background): “Modularity has also been demonstrated to promote adaptation and increase flexibility in response to a changing environment (Kashtan & Alon 2005, Kashtan et al. 2007).”
The Importance of Neurophysiological Constraints for Modelling the Emergence of Modularity (background): “…argued that modularity will prove particularly advantageous when adaptation to changing environments is required (e.g., Lipson, Pollack & Suh, 2002; Kashtan & Alon, 2005), especially since the opportunity to employ useful modules from a rich existing source will invariably be more efficient than…”
Constraints Evolve: Context Dependency of Gene Effects Allows Evolution of Pleiotropy (2015, background): “Multiple groups have modeled the evolution of the structure of GP maps, primarily in the context of the evolution of modularity (Clune et al. 2013, Kashtan & Alon 2005, Le Nagard et al. 2011).”
Illuminating search spaces by mapping elites (2015, methodology): “Networks have to answer whether an object is present on both the left and right sides of the retina 32,44 .”
The evolutionary origins of modularity (2012, methodology): “Given the impracticality of observing modularity evolve in biological systems, we follow most research on the subject by conducting experiments in computational systems with evolutionary dynamics [4,11,13].”
Age-related changes in modular organization of human brain functional networks (2009, positive): “A key advantage of modular organization, which may explain its ubiquity in diverse systems, is that it favours evolutionary and developmental optimization of multiple or changing selection criteria (Redies and Puelles, 2001; Slotine and Lohmiller, 2001; Kashtan and Alon, 2005; Pan and Sinha, 2007): ”
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (2018, positive): “Yet both philosophical arguments [23, 26, 27, 82, 109] and high-profile publications [2, 16, 18 – 21, 52, 59, 60, 83, 85, 86, 92, 141, 146, 148] suggest that digital evolution can be a useful tool to aid and complement the study of biological evolution.”
Selection for Reinforcement-Free Learning Ability as an Organizing Factor in the Evolution of Cognition (2013, background): “Kashtan and Alon [25] evolve neural networks and electronic circuits in a more complex task-switching scenario and find modularization and motif formation in the evolved architectures.”
Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks (2017, methodology): “One of the benefits of modularity is that it allows the components of a system to be easily reconfigured or replaced [34, 36, 38, 39].”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “Indeed, graph representations enabled the growth, depth and breadth of results on real-world complex networks in the last two decades [8–10], with applications spanning biology [26, 27], ecology [28, 29], social science [30, 31], engineering [32, 33], neuroscience [34–36], all the way to cosmology [”
Latent social structure in open source projects (2008, background): “Using such algorithms, recent studies [36, 39] have shown that despite having evolved through random processes, biological networks exhibit design patterns, most notably high modularity.”