‘Infotaxis’ as a strategy for searching without gradients

By Massimo Vergassola, E. Villermaux, Boris I. Shraiman. Nature, 2007

877 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Genetics, Insect Science

View Paper: https://www.semanticscholar.org/paper/64f0592009826d4ab8f677b6d3c001c26224fe12

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Claire B Paris-Limouzy
2. Michael Levin
3. Frantisek Baluska
4. E. Staaterman
5. Friedrich T. Sommer

In Genetics

1. E. Staaterman
2. Claire B Paris-Limouzy
3. Michael Levin
4. Frantisek Baluska
5. E. Villermaux
6. Michel Moreau
7. Olivier Bénichou
8. Julian Lee

In Insect Science

1. Claire B Paris-Limouzy
2. Michael Levin
3. Frantisek Baluska
4. E. Staaterman
5. E. Villermaux
6. P. Banks

Citations in the corpus

• Intermittent search strategies (2011, background): “Vergassola et al. (2007) proposed a search algorithm, which they called ’infotaxis’, designed to work in this case of sparse and fluctuating cues.”
• Odor Landscapes in Turbulent Environments (2014, background): “[44,45]) have shown that extended temporal memory and maps of space”
• Principles of maximum entropy and maximum caliber in statistical physics (2013, background): “…2003; Rhoades et al., 2004; Witkoskie and Cao, 2004, 2008; Cecconi et al., 2005; Lezon et al., 2006; Schneidman et al., 2006; Huang et al., 2007; Vergassola, Villermaux, and Shraiman, 2007; Sanchez and Kondev, 2008; Shahrezaei and Swain, 2008; Southworth and Agard, 2008; Moffitt et al., 2009;…”
• Modelling larval fish navigation: the way forward (2014, methodology): “For example, when the 156 concentration of the odor exceeds a given threshold, larvae could begin orienting towards its source 157 using an infotaxis search strategy (Vergassola et al., 2007).”
• A high-bias, low-variance introduction to Machine Learning for physicists (2018, neutral): “Of particular interest are biophysics inspired works that seek to use RL to understand navigation and sensing in turbulent environments (Colabrese et al., 2017; Masson et al., 2009; Reddy et al., 2016a; Vergassola et al., 2007).”
• On Having No Head: Cognition throughout Biological Systems (2016, positive): “The control algorithm for this behavior has long been the subject of investigation, with respect to the shortterm memory needed for following gradients (Vladimirov and Sourjik, 2009) as well as “infotaxis” policies that do not require gradient sensing (Vergassola et al., 2007).”
• Learning and exploration in action-perception loops (2013, background): “One interesting proposal, put forth by Vergassola et al. suggests that information regarding a reward often falls off with distance as an organism moves away from the source of the reward (Vergassola et al., 2007).”
• Risk vs. reward: how predators and prey respond to aging olfactory cues (2013, background): “2004), even infotaxis (Vergassola et al. 2007) and various mathematical models of search pattern (reviewed in Higgins and Strauss 2004).”
• Locating and quantifying gas emission sources using remotely obtained concentration data (2012, background): “rst et al. (2004)), identifying sources of nuisance odours, and even understanding how moths are able to nd mates by detecting pheromones at concentrations corresponding to individual molecules (e.g. Vergassola et al. (2007)). For greenhouse gases and oil and gas exploration the goal is to locate so”