The wisdom of networks: A general adaptation and learning mechanism of complex systems: The network core triggers fast responses to known stimuli; innovations require the slow network periphery and are encoded by core-remodeling

TL;DR

This paper proposes a core-periphery learning theory where the network core enables fast responses to familiar stimuli, while the periphery facilitates slow adaptation and innovation for novel stimuli across complex systems.

Contribution

It introduces a unified core-periphery learning framework that explains attractor formation, network adaptation, and innovation in neural, protein, social, and other complex networks.

Findings

Core encodes fast responses to known stimuli.

Peripheral network remodeling enables learning of new responses.

The theory links network structure to decision-making and creativity.

Abstract

I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This "core-periphery learning" theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation and a number of recent reports on the adaptation of protein, neuronal and social networks. The coreperiphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision-making processes. Moreover, the power of network periphery-related 'wisdom of crowds' inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion-year evolution.