First Advisor

Christof Teuscher

Date of Publication


Document Type


Degree Name

Master of Science (M.S.) in Computer Science


Computer Science




Boolean networks, Random automata networks, Adaptation and criticality, Complex adaptive systems, Learning and generalization, Network information processing, Machine learning, Neural networks (Computer science), Adaptive control systems



Physical Description

1 online resource (viii, 103 p.) : ill. (some col.)


We extend the study of learning and generalization in feed forward Boolean networks to random Boolean networks (RBNs). We explore the relationship between the learning capability and the network topology, the system size, the training sample size, and the complexity of the computational tasks. We show experimentally that there exists a critical connectivity Kc that improves the generalization and adaptation in networks. In addition, we show that in finite size networks, the critical K is a power-law function of the system size N and the fraction of inputs used during the training. We explain why adaptation improves at this critical connectivity by showing that the network ensemble manifests maximal topological diversity near Kc. Our work is partly motivated by self-assembled molecular and nanoscale electronics. Our findings allow to determine an automata network topology class for efficient and robust information processing.


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Portland State University. Dept. of Computer Science

Persistent Identifier