English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We study information processing in populations of Boolean networks withevolving connectivity and systematically explore the interplay between thelearning capability, robustness, the network topology, and the task complexity.We solve a long-standing open question and find computationally that, for largesystem sizes $N$, adaptive information processing drives the networks to acritical connectivity $K_{c}=2$. For finite size networks, the connectivityapproaches the critical value with a power-law of the system size $N$. We showthat network learning and generalization are optimized near criticality, giventask complexity and the amount of information provided threshold values. Bothrandom and evolved networks exhibit maximal topological diversity near $K_{c}$.We hypothesize that this supports efficient exploration and robustness ofsolutions. Also reflected in our observation is that the variance of the valuesis maximal in critical network populations. Finally, we discuss implications ofour results for determining the optimal topology of adaptive dynamical networksthat solve computational tasks.

Emergent Criticality Through Adaptive Information Processing in Boolean Networks