TY - JOUR
T1 - Quantifying the impact of network structure on speed and accuracy in collective decision-making
AU - Daniels, Bryan C.
AU - Romanczuk, Pawel
N1 - Funding Information:
PR acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC 2002/1 “Science of Intelligence”—project number 390523135, as well as through the Emmy Noether Program, project number RO4766/2-1.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
PY - 2021/11
Y1 - 2021/11
N2 - Found in varied contexts from neurons to ants to fish, binary decision-making is one of the simplest forms of collective computation. In this process, information collected by individuals about an uncertain environment is accumulated to guide behavior at the aggregate scale. We study binary decision-making dynamics in networks responding to inputs with small signal-to-noise ratios, looking for quantitative measures of collectivity that control performance in this task. We find that decision accuracy is directly correlated with the speed of collective dynamics, which is in turn controlled by three factors: the leading eigenvalue of the network adjacency matrix, the corresponding eigenvector’s participation ratio, and distance from the corresponding symmetry-breaking bifurcation. A novel approximation of the maximal attainable timescale near such a bifurcation allows us to predict how decision-making performance scales in large networks based solely on their spectral properties. Specifically, we explore the effects of localization caused by the hierarchical assortative structure of a “rich club” topology. This gives insight into the trade-offs involved in the higher-order structure found in living networks performing collective computations.
AB - Found in varied contexts from neurons to ants to fish, binary decision-making is one of the simplest forms of collective computation. In this process, information collected by individuals about an uncertain environment is accumulated to guide behavior at the aggregate scale. We study binary decision-making dynamics in networks responding to inputs with small signal-to-noise ratios, looking for quantitative measures of collectivity that control performance in this task. We find that decision accuracy is directly correlated with the speed of collective dynamics, which is in turn controlled by three factors: the leading eigenvalue of the network adjacency matrix, the corresponding eigenvector’s participation ratio, and distance from the corresponding symmetry-breaking bifurcation. A novel approximation of the maximal attainable timescale near such a bifurcation allows us to predict how decision-making performance scales in large networks based solely on their spectral properties. Specifically, we explore the effects of localization caused by the hierarchical assortative structure of a “rich club” topology. This gives insight into the trade-offs involved in the higher-order structure found in living networks performing collective computations.
KW - Collective computation
KW - Neural networks
KW - Rich club
KW - Stochastic dynamical systems
KW - Symmetry-breaking transition
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U2 - 10.1007/s12064-020-00335-1
DO - 10.1007/s12064-020-00335-1
M3 - Article
C2 - 33635501
AN - SCOPUS:85101755571
VL - 140
SP - 379
EP - 390
JO - Theory in Biosciences
JF - Theory in Biosciences
SN - 1431-7613
IS - 4
ER -