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Biological and Cognitive Plausibility in Connectionist Networks for Language Modeling


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Anđel, Maja. (2009). Biological and Cognitive Plausibility in Connectionist Networks for Language Modeling. In: 2nd International Conference “The Future of Information Sciences: INFuture2009 – Digital Resources and Knowledge Sharing”, 4-6 November 2009, Zagreb, Croatia.

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If we want to explain cognitive processes with means of connectionist networks, these networks have to correspond with cognitive systems and their underlying biological mechanisms in different respects. The question of biological and cognitive plausibility of connectionist models arises from two different aspects – first, from the aspect of biology – on one hand, one has to have a fair understanding of biological mechanisms and cognitive mechanisms in order to represent them in a model, and on the other hand there is the aspect of modeling – one has to know how to construct a model to represent precisely what we are aiming at. Computer power and modeling techniques have improved dramatically in recent 20 years, so the plausibility problem is being addressed in more adequate ways as well. Connectionist models are often used for representing different aspects of natural language. Their biological plausibility had sometimes been questioned in the past. Today, the field of computational neuroscience offers several acceptable possibilities of modeling higher cognitive functions, and language is among them. This paper brings a presentation of some existing connectionist networks modeling natural language. The question of their explanatory power and plausibility in terms of biological and cognitive systems they are representing is discussed.

Item Type: Published conference work (Lecture)
Uncontrolled Keywords: connectionism, computational neuroscience, language
Subjects: Information sciences > Social-humanistic informatics
Information sciences > Natural language processing, lexicography and encyclopedic science
Departments: Department of German Language and Literature
Date Deposited: 01 Mar 2017 10:05
Last Modified: 01 Mar 2017 10:05

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