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The necessity of connection structures in neural models of variable binding.

van der Velde F, de Kamps M - Cogn Neurodyn (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Technical Cognition, CPE-CTIT, University of Twente, P.O. Box 217, Enschede, 7500 AE The Netherlands ; IO, Leiden University, Leiden, The Netherlands.

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.

No MeSH data available.


The neural sentence representation of Sonnet owns book, by combining a phonological neural blackboard with the neural (sentence) blackboard in Fig. 5. The phonological neural blackboard binds the familiar phonemes/morphemes /so/, /nn/ and /et/ to a ‘word assembly’ W1, which binds to the sentence structure Sonnet owns book (after van der Velde and de Kamps 2006b). The gray ovals and circles are activated by the question “Who owns the book?”
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Fig6: The neural sentence representation of Sonnet owns book, by combining a phonological neural blackboard with the neural (sentence) blackboard in Fig. 5. The phonological neural blackboard binds the familiar phonemes/morphemes /so/, /nn/ and /et/ to a ‘word assembly’ W1, which binds to the sentence structure Sonnet owns book (after van der Velde and de Kamps 2006b). The gray ovals and circles are activated by the question “Who owns the book?”

Mentions: Figure 6 illustrates the interaction between the blackboard for phonological structure and the blackboard for sentence structure in establishing the binding relations in the sentence Sonnet owns a book. Assuming that /so/, /nn/ and /et/ are the phonemes/morphemes of the word Sonnet, they bind to a ‘word assembly’ in the phonological blackboard. In turn, this word assembly can bind to sentence structures. This binding will be regulated by the perception that Sonnet is a noun (van der Velde and de Kamps 2010). In a similar way as illustrated in Fig. 5, the combined blackboard will answer a question like “Who owns the book?” by activating /so/, /nn/ and /et/ in the phonological blackboard (based on the initial activation of the sentence part own book, activated by the question).Fig. 6


The necessity of connection structures in neural models of variable binding.

van der Velde F, de Kamps M - Cogn Neurodyn (2015)

The neural sentence representation of Sonnet owns book, by combining a phonological neural blackboard with the neural (sentence) blackboard in Fig. 5. The phonological neural blackboard binds the familiar phonemes/morphemes /so/, /nn/ and /et/ to a ‘word assembly’ W1, which binds to the sentence structure Sonnet owns book (after van der Velde and de Kamps 2006b). The gray ovals and circles are activated by the question “Who owns the book?”
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4491338&req=5

Fig6: The neural sentence representation of Sonnet owns book, by combining a phonological neural blackboard with the neural (sentence) blackboard in Fig. 5. The phonological neural blackboard binds the familiar phonemes/morphemes /so/, /nn/ and /et/ to a ‘word assembly’ W1, which binds to the sentence structure Sonnet owns book (after van der Velde and de Kamps 2006b). The gray ovals and circles are activated by the question “Who owns the book?”
Mentions: Figure 6 illustrates the interaction between the blackboard for phonological structure and the blackboard for sentence structure in establishing the binding relations in the sentence Sonnet owns a book. Assuming that /so/, /nn/ and /et/ are the phonemes/morphemes of the word Sonnet, they bind to a ‘word assembly’ in the phonological blackboard. In turn, this word assembly can bind to sentence structures. This binding will be regulated by the perception that Sonnet is a noun (van der Velde and de Kamps 2010). In a similar way as illustrated in Fig. 5, the combined blackboard will answer a question like “Who owns the book?” by activating /so/, /nn/ and /et/ in the phonological blackboard (based on the initial activation of the sentence part own book, activated by the question).Fig. 6

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Technical Cognition, CPE-CTIT, University of Twente, P.O. Box 217, Enschede, 7500 AE The Netherlands ; IO, Leiden University, Leiden, The Netherlands.

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.

No MeSH data available.