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Unifying Time to Contact Estimation and Collision Avoidance across Species

Overview of attention for article published in PLoS Computational Biology, August 2012
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Title
Unifying Time to Contact Estimation and Collision Avoidance across Species
Published in
PLoS Computational Biology, August 2012
DOI 10.1371/journal.pcbi.1002625
Pubmed ID
Authors

Matthias S. Keil, Joan López-Moliner

Abstract

The τ-function and the η-function are phenomenological models that are widely used in the context of timing interceptive actions and collision avoidance, respectively. Both models were previously considered to be unrelated to each other: τ is a decreasing function that provides an estimation of time-to-contact (ttc) in the early phase of an object approach; in contrast, g has a maximum before ttc. Furthermore, it is not clear how both functions could be implemented at the neuronal level in a biophysically plausible fashion. Here we propose a new framework--the corrected modified Tau function--capable of predicting both τ-type ("τ(cm)") and g-type ("t(mod)") responses. The outstanding property of our new framework is its resilience to noise. We show that t(mod) can be derived from a firing rate equation, and, as g, serves to describe the response curves of collision sensitive neurons. Furthermore, we show that tcm predicts the psychophysical performance of subjects determining ttc. Our new framework is thus validated successfully against published and novel experimental data. Within the framework, links between τ-type and η-type neurons are established. Therefore, it could possibly serve as a model for explaining the co-occurrence of such neurons in the brain.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 3%
United States 1 3%
China 1 3%
Brazil 1 3%
Unknown 36 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 33%
Researcher 8 20%
Student > Master 5 13%
Student > Doctoral Student 3 8%
Professor > Associate Professor 3 8%
Other 5 13%
Unknown 3 8%
Readers by discipline Count As %
Psychology 10 25%
Engineering 6 15%
Neuroscience 5 13%
Computer Science 4 10%
Agricultural and Biological Sciences 3 8%
Other 8 20%
Unknown 4 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 September 2012.
All research outputs
#17,302,400
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#7,481
of 8,964 outputs
Outputs of similar age
#116,600
of 174,095 outputs
Outputs of similar age from PLoS Computational Biology
#79
of 101 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.