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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

It has long been believed that the brain is highly modular both in terms ofstructure and function, although recent evidence has led some to question theextent of both types of modularity. We used artificial neural networks to testthe hypothesis that structural modularity is sufficient to guarantee functionalspecialization, and find that in general, this doesn't necessarily hold exceptat extreme levels. We then systematically tested which features of theenvironment and network do lead to the emergence of specialization. We used asimple toy environment, task and network, allowing us precise control, and showthat in this setup, several distinct measures of specialization givequalitatively similar results. We further find that (1) specialization can onlyemerge in environments where features of that environment are meaningfullyseparable, (2) specialization preferentially emerges when the network isstrongly resource-constrained, and (3) these findings are qualitatively similaracross different network architectures, but the quantitative relationshipsdepends on the architecture type. Finally, we show that functionalspecialization varies dynamically across time, and demonstrate that thesedynamics depend on both the timing and bandwidth of information flow in thenetwork. We conclude that a static notion of specialization, based onstructural modularity, is likely too simple a framework for understandingintelligence in situations of real-world complexity, from biology tobrain-inspired neuromorphic systems. We propose that thoroughly stress testingcandidate definitions of functional modularity in simplified scenarios beforeextending to more complex data, network models and electrophysiologicalrecordings is likely to be a fruitful approach.

Dynamics of specialization in neural modules under resource constraints