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.
Keywords: Collective computation; Neural networks; Rich club; Stochastic dynamical systems; Symmetry-breaking transition.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.