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Implementation of Olfactory Bulb Glomerular-Layer Computations in a Digital Neurosynaptic Core

Overview of attention for article published in Frontiers in Neuroscience, January 2012
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Title
Implementation of Olfactory Bulb Glomerular-Layer Computations in a Digital Neurosynaptic Core
Published in
Frontiers in Neuroscience, January 2012
DOI 10.3389/fnins.2012.00083
Pubmed ID
Authors

Nabil Imam, Thomas A. Cleland, Rajit Manohar, Paul A. Merolla, John V. Arthur, Filipp Akopyan, Dharmendra S. Modha

Abstract

We present a biomimetic system that captures essential functional properties of the glomerular layer of the mammalian olfactory bulb, specifically including its capacity to decorrelate similar odor representations without foreknowledge of the statistical distributions of analyte features. Our system is based on a digital neuromorphic chip consisting of 256 leaky-integrate-and-fire neurons, 1024 × 256 crossbar synapses, and address-event representation communication circuits. The neural circuits configured in the chip reflect established connections among mitral cells, periglomerular cells, external tufted cells, and superficial short-axon cells within the olfactory bulb, and accept input from convergent sets of sensors configured as olfactory sensory neurons. This configuration generates functional transformations comparable to those observed in the glomerular layer of the mammalian olfactory bulb. Our circuits, consuming only 45 pJ of active power per spike with a power supply of 0.85 V, can be used as the first stage of processing in low-power artificial chemical sensing devices inspired by natural olfactory systems.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
United Kingdom 2 3%
Australia 1 1%
Greece 1 1%
Portugal 1 1%
Unknown 69 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 29%
Researcher 16 21%
Student > Master 13 17%
Student > Bachelor 6 8%
Professor 5 6%
Other 12 15%
Unknown 3 4%
Readers by discipline Count As %
Engineering 23 29%
Agricultural and Biological Sciences 19 24%
Computer Science 11 14%
Neuroscience 8 10%
Physics and Astronomy 4 5%
Other 7 9%
Unknown 6 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 31 October 2013.
All research outputs
#16,048,009
of 25,374,647 outputs
Outputs from Frontiers in Neuroscience
#7,064
of 11,542 outputs
Outputs of similar age
#163,297
of 250,100 outputs
Outputs of similar age from Frontiers in Neuroscience
#96
of 154 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 250,100 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.