Connection Strategies in Associative Memory Models

W. Chen; R. Maex; R.G. Adams; L. Calcraft; Volker Steuber; N. Davey

doi:10.1007/978-3-642-04921-7_5

Connection Strategies in Associative Memory Models

W. Chen, R. Maex, R.G. Adams, L. Calcraft, Volker Steuber, N. Davey

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

94 Downloads (Pure)

Abstract

The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks.

Original language	English
Pages (from-to)	42-51
Journal	Lecture Notes in Computer Science (LNCS)
Volume	5495
DOIs	https://doi.org/10.1007/978-3-642-04921-7_5
Publication status	Published - 2009

Access to Document

10.1007/978-3-642-04921-7_5

903597.pdf

Cite this

@article{cd2b64c1f845411ab765401644ed2bb5,

title = "Connection Strategies in Associative Memory Models",

abstract = "The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks.",

author = "W. Chen and R. Maex and R.G. Adams and L. Calcraft and Volker Steuber and N. Davey",

note = "“The original publication is available at www.springerlink.com”. Copyright Springer.",

year = "2009",

doi = "10.1007/978-3-642-04921-7_5",

language = "English",

volume = "5495",

pages = "42--51",

journal = "Lecture Notes in Computer Science (LNCS)",

issn = "0302-9743",

publisher = "Springer Nature",

}

TY - JOUR

T1 - Connection Strategies in Associative Memory Models

AU - Chen, W.

AU - Maex, R.

AU - Adams, R.G.

AU - Calcraft, L.

AU - Steuber, Volker

AU - Davey, N.

N1 - “The original publication is available at www.springerlink.com”. Copyright Springer.

PY - 2009

Y1 - 2009

N2 - The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks.

AB - The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks.

U2 - 10.1007/978-3-642-04921-7_5

DO - 10.1007/978-3-642-04921-7_5

M3 - Article

SN - 0302-9743

VL - 5495

SP - 42

EP - 51

JO - Lecture Notes in Computer Science (LNCS)

JF - Lecture Notes in Computer Science (LNCS)

ER -

Connection Strategies in Associative Memory Models

Abstract

Access to Document

Fingerprint

Cite this