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The necessity of connection structures in neural models of variable binding

Overview of attention for article published in Cognitive Neurodynamics, February 2015
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Title
The necessity of connection structures in neural models of variable binding
Published in
Cognitive Neurodynamics, February 2015
DOI 10.1007/s11571-015-9331-7
Pubmed ID
Authors

Frank van der Velde, Marc de Kamps

Abstract

In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1-11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other ('connectivity based') type uses dedicated connections structures to achieve novel variable binding. Feldman argued that label (synchrony) based models are the only possible candidates to handle novel variable binding, whereas connectivity based models lack the flexibility required for that. We argue and illustrate that Feldman's analysis is incorrect. Contrary to his conclusion, connectivity based models are the only viable candidates for models of novel variable binding because they are the only type of models that can produce behavior. We will show that the label (synchrony) based models analyzed by Feldman are in fact examples of connectivity based models. Feldman's analysis that novel variable binding can be achieved without existing connection structures seems to result from analyzing the binding problem in a wrong frame of reference, in particular in an outside instead of the required inside frame of reference. Connectivity based models can be models of novel variable binding when they possess a connection structure that resembles a small-world network, as found in the brain. We will illustrate binding with this type of model with episode binding and the binding of words, including novel words, in sentence structures.

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Geographical breakdown

Country Count As %
Australia 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 26%
Student > Master 3 13%
Lecturer > Senior Lecturer 2 9%
Professor > Associate Professor 2 9%
Lecturer 1 4%
Other 5 22%
Unknown 4 17%
Readers by discipline Count As %
Psychology 5 22%
Neuroscience 4 17%
Computer Science 3 13%
Business, Management and Accounting 1 4%
Agricultural and Biological Sciences 1 4%
Other 4 17%
Unknown 5 22%
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 11 February 2015.
All research outputs
#18,397,742
of 22,788,370 outputs
Outputs from Cognitive Neurodynamics
#195
of 319 outputs
Outputs of similar age
#260,583
of 357,415 outputs
Outputs of similar age from Cognitive Neurodynamics
#8
of 11 outputs
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