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A working memory model for serial order that stores information in the intrinsic excitability properties of neurons

Overview of attention for article published in Journal of Computational Neuroscience, March 2013
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Title
A working memory model for serial order that stores information in the intrinsic excitability properties of neurons
Published in
Journal of Computational Neuroscience, March 2013
DOI 10.1007/s10827-013-0447-7
Pubmed ID
Authors

Eduardo Conde-Sousa, Paulo Aguiar

Abstract

Models for temporary information storage in neuronal populations are dominated by mechanisms directly dependent on synaptic plasticity. There are nevertheless other mechanisms available that are well suited for creating short-term memories. Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. For this we introduce a functional class of neurons, named gate interneurons, which can store information in their membrane dynamics and can literally act as gates routing the flow of activations in the principal neurons population. The presented model exhibits properties which are in close agreement with experimental results in working memory. Namely, the recall process plays an important role in stabilizing and prolonging the memory trace. This means that the stored information is correctly maintained as long as it is being used. Moreover, the working memory model is adequate for storing completely new information, in time windows compatible with the notion of "one-shot" learning (hundreds of milliseconds).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 53 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 28%
Researcher 9 17%
Student > Master 9 17%
Student > Bachelor 4 7%
Professor 3 6%
Other 9 17%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 24%
Neuroscience 9 17%
Physics and Astronomy 6 11%
Mathematics 5 9%
Psychology 4 7%
Other 11 20%
Unknown 6 11%
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 02 April 2013.
All research outputs
#18,333,600
of 22,703,044 outputs
Outputs from Journal of Computational Neuroscience
#222
of 306 outputs
Outputs of similar age
#150,586
of 198,759 outputs
Outputs of similar age from Journal of Computational Neuroscience
#1
of 3 outputs
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So far Altmetric has tracked 306 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
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