摘要:

One of the fundamental difficulties facing a child trying to acquire a language is that the association between meanings and sounds is for the most part an arbitrary one. In this work, we model this process using a recurrent neural network that is trained to map a set of plan vectors, representing meaning, to associated sequences of phonemes, representing the phonological structure of the surface forms. We evaluate the role of the similarity structure of the target forms (the adult vocabulary) and the similarity structure of the input forms (the semantic structure) on the evolution of the network's vocabulary. The model's performance offers a principled account of various phenomena associated with children's early vocabulary development including the difficulty of acquiring synonyms, the appearance of idiosyncratic forms and over-extension errors. The model makes several unexplored predictions for the developmental profiles of young children acquiring morphology.

ISSN:0954-0091    

DOI:10.1080/09540099408915731

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

Evidence from priming studies indicates that both semantic and associative relations between pairs of words facilitate word recognition, and that pairs of words related in both ways (e.g. hammer-nail, cat-dog) produce an additional ‘associative boost’. We argue that while semantic priming may result from overlapping patterns of micro-features in a distributed memory model (e.g. Masson, 1991), associative priming is a result of frequent co-occurrence of words in the language. We describe a simple recurrent network, with distributed phonological and semantic representations, which is sensitive to the sequential occurrence of phonological patterns during training, and which produces associative facilitation of word recognition in a simulation of the priming task.

ISSN:0954-0091    

DOI:10.1080/09540099408915732

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

In this paper, we propose an extension to the recursive auto-associative memory (RAAM) by Pollack. This extension, the labelling RAAM (LRAAM), can encode labelled graphs with cycles by representing pointers explicitly. Some technical problems encountered in the RAAM, such as the termination problem in the learning and decoding processes, are solved more naturally in the LRAAM framework. The representations developed for the pointers seem to be robust to recurrent decoding along a cycle. Theoretical and experimental results show that the performances of the proposed learning scheme depend on the way the graphs are represented in the training set. Critical features for the representation are cycles and confluent pointers. Data encoded in a LRAAM can be accessed by a pointer as well as by content. Direct access by content can be achieved by transforming the encoder network of the LRAAM into a particular bidirectional associative memory (BAM). Statistics performed on different instances of LRAAM show a strict connection between the associated BAM and a standard BAM. Different access procedures can be defined depending on the access key. The access procedures are not wholly reliable; however, they seem to have a good success rate. The generalization test for the RAAM is no longer complete for the LRAAM. Some suggestions on how to solve this problem are given. Some results on modular LRAAM, stability and application to neural dynamics control are summarized.

ISSN:0954-0091    

DOI:10.1080/09540099408915733

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

Classic barriers to using auto-associative neural networks to model mammalian memory include the unrealistically high synaptic connectivity of fully connected networks, and the relative paucity of information that has been stored in networks with realistic numbers of synapses per neuron and learning rules amenable to physiological implementation. We describe extremely large, auto-associative networks with low synaptic density. The networks have no direct connections between neurons of the same layer. Rather, the neurons of one layer are 'linked' by connections to neurons of some other layer. Patterns of projections of one layer on to another which form projective planes, or other cognate geometries, confer considerable computational power an the network.

ISSN:0954-0091    

DOI:10.1080/09540099408915734

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

ISSN:0954-0091    

DOI:10.1080/09540099408915735

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor