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Timing over Tuning: Overcoming the Shortcomings of a Line Attractor during a Working Memory Task

Overview of attention for article published in PLoS Computational Biology, January 2014
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Title
Timing over Tuning: Overcoming the Shortcomings of a Line Attractor during a Working Memory Task
Published in
PLoS Computational Biology, January 2014
DOI 10.1371/journal.pcbi.1003437
Pubmed ID
Authors

Jonathan D. Drover

Abstract

How the brain stores information about a sensory stimulus in working memory is not completely known. Clues about the mechanisms responsible for working memory can be gleaned by recording from neurons during the performance of a delayed response task. I focus on the data recorded during such an experiment, a classic tactile discrimination task. I describe how the observed variability in the firing rate during a trial suggests that the type of attractor that is responsible for holding the stimulus information is not a fixed-point type attractor. I propose an alternate mechanism to a line attractor that allows the network to hold the value of an analog stimulus variable for the duration of the delay period, but rather than maintain a constant level of activity, the cells' firing rate varies throughout the delay period. I describe how my proposed mechanism offers a substantial advantage over a line attractor: The tuning requirements of cell to cell connections are greatly eased from that of a line attractor. To accommodate a change in the length of the delay period, I show that the network can be altered by changing a single parameter--the timing of an executive signal that originates outside of the network. To demonstrate the mechanism, as well as the tuning benefits, I use a well known model of propagation in neuronal networks.

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Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 4%
Spain 1 4%
United States 1 4%
Italy 1 4%
Unknown 22 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 31%
Student > Ph. D. Student 6 23%
Student > Master 4 15%
Professor > Associate Professor 3 12%
Student > Bachelor 2 8%
Other 2 8%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 31%
Psychology 4 15%
Physics and Astronomy 3 12%
Computer Science 3 12%
Engineering 3 12%
Other 3 12%
Unknown 2 8%