Collective dynamics and model verification: Connecting kinetic modeling to data

Decision-making without a brain: the case of the slime mold Physarum polycephalum

Simon Garnier

NJIT and Rutgers University

Should I exploit well-known options, or do I risk further exploration for potentially higher reward? This is the exploration-exploitation tradeoff, and while it faces casino gamblers and foraging organisms alike, the optimal solution is unknown. The tradeoff has been studied using the 2-Armed Bandit problem, where a player aims to maximize their gain when faced with two slot machines, each with a distinct but unknown reward rate. Studies thus far have only used organisms with brains. We tested the slime mold Physarum polycephalum with the 2-Armed Bandit problem by assessing the effect of sampling on foraging patch choice in a T-maze. We generate insight into the basic processes of decision making in a cell, including the use of relative vs absolute reward criteria (in both the frequency of reward, and the combination of frequency and magnitude), and the effect of static vs dynamic exploration environments. We propose several biologically plausible decision criteria the organism may be using and, through Bayesian inference, determine which of these models best explains the empirical data. We challenge the common view that neurological hardware is required to solve complex problems.