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Of Toasters and Molecular Ticker Tapes

Overview of attention for article published in PLoS Computational Biology, December 2011
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1 news outlet
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4 blogs
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12 X users
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1 Facebook page
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4 Wikipedia pages
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1 Google+ user

Citations

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Readers on

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125 Mendeley
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1 CiteULike
Title
Of Toasters and Molecular Ticker Tapes
Published in
PLoS Computational Biology, December 2011
DOI 10.1371/journal.pcbi.1002291
Pubmed ID
Authors

Konrad P. Kording

Abstract

Experiments in systems neuroscience can be seen as consisting of three steps: (1) selecting the signals we are interested in, (2) probing the system with carefully chosen stimuli, and (3) getting data out of the brain. Here I discuss how emerging techniques in molecular biology are starting to improve these three steps. To estimate its future impact on experimental neuroscience, I will stress the analogy of ongoing progress with that of microprocessor production techniques. These techniques have allowed computers to simplify countless problems; because they are easier to use than mechanical timers, they are even built into toasters. Molecular biology may advance even faster than computer speeds and has made immense progress in understanding and designing molecules. These advancements may in turn produce impressive improvements to each of the three steps, ultimately shifting the bottleneck from obtaining data to interpreting it.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 9%
France 2 2%
Colombia 1 <1%
Germany 1 <1%
Austria 1 <1%
Portugal 1 <1%
United Kingdom 1 <1%
Israel 1 <1%
Unknown 106 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 29%
Researcher 24 19%
Student > Bachelor 13 10%
Student > Doctoral Student 9 7%
Student > Master 9 7%
Other 25 20%
Unknown 9 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 39%
Neuroscience 24 19%
Engineering 14 11%
Computer Science 10 8%
Psychology 5 4%
Other 11 9%
Unknown 12 10%