Clustering predicts memory performance in networks of spiking and non-spiking neurons

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

doi:10.3389/fncom.2011.00014

Clustering predicts memory performance in networks of spiking and non-spiking neurons

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

Biocomputation Research Group

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

6 Citations (Scopus)

173 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 that 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. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong negative linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

Original language	English
Article number	14
Journal	Frontiers in Computational Neuroscience
Volume	5
DOIs	https://doi.org/10.3389/fncom.2011.00014
Publication status	Published - 2011

Keywords

perception
learning
associative memory
small-world network
non-random graph
connectivity

Access to Document

10.3389/fncom.2011.00014

905361
Original article can be found at : http://www.frontiersin.org/ "This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission." "Copyright : 2011 Chen, Maex, Adams, Steuber, Calcraft and Davey. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with."
Final published version, 1.28 MB

Cite this

@article{a383337a298f44569b946ae51e496362,

title = "Clustering predicts memory performance in networks of spiking and non-spiking neurons",

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 that 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. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong negative linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.",

keywords = "perception, learning, associative memory, small-world network, non-random graph, connectivity",

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

note = "Original article can be found at : http://www.frontiersin.org/ {"}This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.{"} {"}Copyright : 2011 Chen, Maex, Adams, Steuber, Calcraft and Davey. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.{"}",

year = "2011",

doi = "10.3389/fncom.2011.00014",

language = "English",

volume = "5",

journal = "Frontiers in Computational Neuroscience",

issn = "1662-5188",

publisher = "Frontiers Media S.A.",

}

TY - JOUR

T1 - Clustering predicts memory performance in networks of spiking and non-spiking neurons

AU - Chen, W.

AU - Maex, R.

AU - Adams, R.G.

AU - Steuber, Volker

AU - Calcraft, L.

AU - Davey, N.

N1 - Original article can be found at : http://www.frontiersin.org/ "This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission." "Copyright : 2011 Chen, Maex, Adams, Steuber, Calcraft and Davey. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with."

PY - 2011

Y1 - 2011

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 that 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. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong negative linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

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 that 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. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong negative linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

KW - perception

KW - learning

KW - associative memory

KW - small-world network

KW - non-random graph

KW - connectivity

U2 - 10.3389/fncom.2011.00014

DO - 10.3389/fncom.2011.00014

M3 - Article

SN - 1662-5188

VL - 5

JO - Frontiers in Computational Neuroscience

JF - Frontiers in Computational Neuroscience

M1 - 14

ER -

Clustering predicts memory performance in networks of spiking and non-spiking neurons

Abstract

Keywords

Access to Document

Fingerprint

Cite this