scholarly journals Attractor Hypothesis of Associative Cortex: Insights from a Biophysically Detailed Network Model

10.5772/56229 ◽  
2013 ◽  
Author(s):  
Mikael Lundqvist ◽  
Pawel Herman ◽  
Anders Lansner
Keyword(s):  
Network Model ◽  
Associative Cortex

A Network Model for Optimizing two Echelon Multi-Item Repair Level Decisions

1991 ◽  
Vol 8 (1) ◽  
pp. 77-90
Author(s):  
W. Steven Demmy ◽  
Lawrence Briskin
Keyword(s):  
Network Model

Explaining false memory with a network model

1997 ◽  
Author(s):  
Kisok Kim
Keyword(s):  
False Memory ◽  
Network Model

A neural network model of entropy discrimination in pigeons

2003 ◽  
Author(s):  
Christopher Silansky ◽  
Anthony Chemero
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model

A neural network model of pre-pulse inhibition

2003 ◽  
Author(s):  
Nestor Schmajuk ◽  
Roger Smith
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model

Nerdalicious scientainment: A network analysis of English libfixes

2019 ◽  
Vol 12 (3) ◽  
pp. 353-384 ◽  
Author(s):  
Muriel Norde ◽  
Sarah Sippach
Keyword(s):  
Network Analysis ◽  
Network Model ◽  
Phonological Similarity ◽  
High Type ◽  
Type Frequency ◽  
Semantic Properties

Libfixes are parts of words that share properties with both blends, compounds and affixes. They are deliberate formations, often with a jocular character, e.g. nerdalicious ‘delicious for nerds’, or scientainment ‘scientific entertainment’. These are not one-off formations – some libfixes have become very productive, as evidenced by high type frequency in a single corpus. Libfix constructions are particularly interesting for a network analysis for three reasons: they do not always have discrete morpheme boundaries, they feature a wide variety of bases (including phrases, as in give-me-a-break-o-meter), and they may be the source of back formations such as infotain. In this paper, we present a corpus-based analysis of eight English libfixes (cracy, fection, flation, gasm, licious, (o-)meter, tainment, and tastic), detailing their formal and semantic properties, as well as their differences and similarities. We argue that libfixes are most fruitfully analysed in a Bybeean network model, in which nodes are connected on the basis of phonological similarity, which allows for both fully compositional and non-compositional constructions to be linked without an exhaustive analysis into morphemes.

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