2011/04/30 by Alireza Goudarzi, Christof Teuscher, Natali Gulbahce +1 · 48 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Algorithm #Boolean function #Boolean network #Cellular Automata and Applications #Computer science #Criticality #Evolutionary Algorithms and Applications #Gene Regulatory Network Analysis #Nuclear physics #Physics #Statistical physics #Theoretical computer science #cond-mat.dis-nn #cs.NE #nlin.AO
paper · pdf · doi:10.1103/physrevlett.108.128702
published in Physical Review Letters 108(12), 128702 (American Physical Society) · 5 pages, 4 figures
openalex publication_date 2012/03/23 · arxiv created 2012/04/26 · arxiv updated 2015/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We study information processing in populations of boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity K(c)=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both random and evolved networks exhibit maximal topological diversity near K(c). We hypothesize that this diversity supports efficient exploration and robustness of solutions. Also reflected in our observation is that the variance of the fitness values is maximal in critical network populations. Finally, we discuss implications of our results for determining the optimal topology of adaptive dynamical networks that solve computational tasks.