Optimal sensor selection for noisy binary detection in stochastic pooling networks

Abstract

Stochastic Pooling Networks (SPNs) are a useful model for understanding and explaining how naturally occurring encoding of stochastic processes can occur in sensor systems ranging from macroscopic social networks to neuron populations and nanoscale electronics. Due to the interaction of nonlinearity, random noise, and redundancy, SPNs support various unexpected emergent features, such as suprathreshold stochastic resonance, but most existing mathematical results are restricted to the simplest case where all sensors in a network are identical. Nevertheless, numerical results on information transmission have shown that in the presence of independent noise, the optimal configuration of a SPN is such that there should be partial heterogeneity in sensor parameters, such that the optimal solution includes clusters of identical sensors, where each cluster has different parameter values. In this paper, we consider a SPN model of a binary hypothesis detection task and show mathematically that the optimal solution for a specific bound on detection performance is also given by clustered heterogeneity, such that measurements made by sensors with identical parameters either should all be excluded from the detection decision or all included. We also derive an algorithm for numerically finding the optimal solution and illustrate its utility with several examples, including a model of parallel sensory neurons with Poisson firing characteristics.