Published In

Physical Review Letters

Document Type

Article

Publication Date

3-1-2012

Subjects

Boolean networks, Adaptive computing systems, Adaptive control systems

Abstract

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.

Description

This is the publisher's final pdf. Article appears in Physical Review Letters (http://prl.aps.org/) and is copyrighted by APS Journals (http://publish.aps.org/).

DOI

10.1103/PhysRevLett.108.128702

Persistent Identifier

http://archives.pdx.edu/ds/psu/7660

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