Finding structure in time

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Finding structure in time (1990)

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by Jeffrey L. Elman

Venue:	COGNITIVE SCIENCE
Citations:	2018 - 22 self

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@ARTICLE{Elman90findingstructure,
    author = {Jeffrey L. Elman},
    title = {Finding structure in time},
    journal = {COGNITIVE SCIENCE},
    year = {1990},
    volume = {14},
    number = {2},
    pages = {179--211}
}

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Abstract

Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction.

Keyphrases

task demand recurrent link interesting internal representation temporal version task processing prior internal state type token distinction syntactic semantic feature spatial representation memory demand internal representation many interesting human behavior rich structure connectionist model simple problem hidden unit pattern lexical category dynamic memory current report

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