Systems Science Friday Noon Seminar Series



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Random Boolean networks (RBN) are discrete dynamical systems composed of N automata with a binary state, each of which interacts with other automata in the network. RBNs were originally introduced as simplified models of gene regulation. In this presentation, I will present recent work done conjointly with Natali Gulbahce (UCSF), Thimo Rohlf (MPI, CNRS), and Christof Teuscher (PSU). We extend the study of learning in feedforward Boolean networks to random Boolean networks (RBNs) and systematically explore the relationship between the learning capability, the network topology, the system size N, the training sample T, and the complexity of the computational task. We find experimentally that for large system sizes N, there exists a critical connectivity Kc=2 that improves the learning in networks. We show that in finite size networks, the critical Kc scales as a power law of the system size N and the training sample T. During the learning process, the in-degree distribution evolves from a Poissonian to an exponential distribution. The improved learning capability is explained by a maximal topological diversity near Kc. Our findings have important implications for determining the optimal topology of complex dynamical networks that solve specific computational tasks.

Biographical Information

Alireza Goudarzi is a second-year Master's student of both Computer Science and System Science programs at Portland State University. He is currently working on his Master's thesis in Computer Science under Christof Teuscher and in Systems Science under George Lendaris. His research interests include neural information processing, contextual learning, and alternative computer architectures. His project "Information Processing in Random Boolean Networks" was awarded the third prize in 2010 Columbia-Willamette Chapter of Sigma Xi. His conjoint works with Christof Teusher (PSU), Natali Gulbahce (UCSF), and Thimo Rohlf (MPI) appeared in the book Theoretical and Technological Advancements in Nanotechnology and Molecular Computation: Interdisciplinary Gains by B. MacLennan. Last December he presented his paper coauthored with Christof Teuscher and Natali Gulbahce titled "Learning and Generalization in Random Automata Networks," at the 5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS 2010) in Boston.


System theory, Computational complexity, Boolean algebra, Cellular automata, Machine learning, Genetic algorithms, Topology


Computer Sciences

Persistent Identifier

On the Effect of Criticality and Topology on Learning in Random Boolean Networks