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Recurrent temporal networks and language acquisition—from corticostriatal neurophysiology to reservoir computing

Overview of attention for article published in Frontiers in Psychology, January 2013
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Title
Recurrent temporal networks and language acquisition—from corticostriatal neurophysiology to reservoir computing
Published in
Frontiers in Psychology, January 2013
DOI 10.3389/fpsyg.2013.00500
Pubmed ID
Authors

Peter F. Dominey

Abstract

One of the most paradoxical aspects of human language is that it is so unlike any other form of behavior in the animal world, yet at the same time, it has developed in a species that is not far removed from ancestral species that do not possess language. While aspects of non-human primate and avian interaction clearly constitute communication, this communication appears distinct from the rich, combinatorial and abstract quality of human language. So how does the human primate brain allow for language? In an effort to answer this question, a line of research has been developed that attempts to build a language processing capability based in part on the gross neuroanatomy of the corticostriatal system of the human brain. This paper situates this research program in its historical context, that begins with the primate oculomotor system and sensorimotor sequencing, and passes, via recent advances in reservoir computing to provide insight into the open questions, and possible approaches, for future research that attempts to model language processing. One novel and useful idea from this research is that the overlap of cortical projections onto common regions in the striatum allows for adaptive binding of cortical signals from distinct circuits, under the control of dopamine, which has a strong adaptive advantage. A second idea is that recurrent cortical networks with fixed connections can represent arbitrary sequential and temporal structure, which is the basis of the reservoir computing framework. Finally, bringing these notions together, a relatively simple mechanism can be built for learning the grammatical constructions, as the mappings from surface structure of sentences to their meaning. This research suggests that the components of language that link conceptual structure to grammatical structure may be much simpler that has been proposed in other research programs. It also suggests that part of the residual complexity is in the conceptual system itself.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
France 2 3%
United Kingdom 2 3%
Netherlands 1 1%
Germany 1 1%
Unknown 64 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 26%
Researcher 17 23%
Student > Master 11 15%
Student > Bachelor 4 5%
Professor > Associate Professor 4 5%
Other 13 18%
Unknown 5 7%
Readers by discipline Count As %
Neuroscience 18 25%
Psychology 11 15%
Computer Science 9 12%
Agricultural and Biological Sciences 5 7%
Linguistics 4 5%
Other 17 23%
Unknown 9 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 November 2015.
All research outputs
#14,327,650
of 24,943,708 outputs
Outputs from Frontiers in Psychology
#13,237
of 33,669 outputs
Outputs of similar age
#166,358
of 292,957 outputs
Outputs of similar age from Frontiers in Psychology
#517
of 969 outputs
Altmetric has tracked 24,943,708 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 33,669 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.1. This one has gotten more attention than average, scoring higher than 59% of its peers.
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 292,957 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 969 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.