Maximally informative foraging by Caenorhabditis elegans.

Adam J Calhoun,Sreekanth H Chalasani,Tatyana O Sharpee

doi:10.7554/elife.04220

Adam J Calhoun, Sreekanth H Chalasani + Show 1 more

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https://doi.org/10.7554/elife.04220

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Abstract

Animals have evolved intricate search strategies to find new sources of food. Here, we analyze a complex food seeking behavior in the nematode Caenorhabditis elegans (C. elegans) to derive a general theory describing different searches. We show that C. elegans, like many other animals, uses a multi-stage search for food, where they initially explore a small area intensively ('local search') before switching to explore a much larger area ('global search'). We demonstrate that these search strategies as well as the transition between them can be quantitatively explained by a maximally informative search strategy, where the searcher seeks to continuously maximize information about the target. Although performing maximally informative search is computationally demanding, we show that a drift-diffusion model can approximate it successfully with just three neurons. Our study reveals how the maximally informative search strategy can be implemented and adopted to different search conditions.

Highlights

In considering animal behavior and decision-making, it is exciting to consider the proposal (Polani, 2009; Tishby and Polani, 2011) that animals may be guided by fundamental statistical quantities, such as the maximization of Shannon mutual information (Cover and Thomas, 1991)
For the sake of simplicity, we assume that the probability of finding food is initially distributed as a two-dimensional Gaussian distribution, which imposes the minimal structural constraint beyond the variance of the spatial distribution (Jaynes, 2003). When searching through this space, an animal using the maximally informative trajectory should move in the direction that maximizes its information about the location of food. This can be calculated by taking into account the probability that the nearby environment would emit food odor and estimating the change in information the animal expects will result from any detection or non-detection events (Vergassola et al, 2007)
The drift rate depends primarily on the ratio σ/L (Figure 6E–F). These results demonstrate that maximally informative foraging trajectories can be approximated by a simple drift-diffusion model across a range of behaviorally relevant conditions

Summary

Introduction

In considering animal behavior and decision-making, it is exciting to consider the proposal (Polani, 2009; Tishby and Polani, 2011) that animals may be guided by fundamental statistical quantities, such as the maximization of Shannon mutual information (Cover and Thomas, 1991). The fact that mutual information can be used with different types of probability distributions makes it possible to quantitatively compare the efficiency of behavioral decisions across species, sensory modalities, and tasks This idea has already yielded insights into diverse behaviors including human eye movements patterns (Najemnik and Geisler, 2005) and animal navigation in a turbulent environment (Vergassola et al, 2007; Masson et al, 2009). Both these patterns of behavior can be accounted for by adapting a maximally informative search strategy to the appropriate behavioral context. When the information content of the environment is very high, such as when chemical gradients can be tracked reliably, strategies based on information

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