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Learning synaptic clusters for non-linear dendritic processing

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Learning synaptic clusters for non-linear dendritic processing. / Spratling, Michael W.; Hayes, Gillian M.

In: NEURAL PROCESSING LETTERS, Vol. 11, No. 1, N/A, 02.2000, p. 17 - 27.

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

Harvard

Spratling, MW & Hayes, GM 2000, 'Learning synaptic clusters for non-linear dendritic processing', NEURAL PROCESSING LETTERS, vol. 11, no. 1, N/A, pp. 17 - 27. https://doi.org/10.1023/A:1009634821039

APA

Spratling, M. W., & Hayes, G. M. (2000). Learning synaptic clusters for non-linear dendritic processing. NEURAL PROCESSING LETTERS, 11(1), 17 - 27. [N/A]. https://doi.org/10.1023/A:1009634821039

Vancouver

Spratling MW, Hayes GM. Learning synaptic clusters for non-linear dendritic processing. NEURAL PROCESSING LETTERS. 2000 Feb;11(1):17 - 27. N/A. https://doi.org/10.1023/A:1009634821039

Author

Spratling, Michael W. ; Hayes, Gillian M. / Learning synaptic clusters for non-linear dendritic processing. In: NEURAL PROCESSING LETTERS. 2000 ; Vol. 11, No. 1. pp. 17 - 27.

Bibtex Download

@article{e40f782fa93e419097a8ba7ace724819,
title = "Learning synaptic clusters for non-linear dendritic processing",
abstract = "Nonlinear dendritic processing appears to be a feature of biological neurons and would also be of use in many applications of artificial neural networks. This paper presents a model of an initially standard linear node which uses unsupervised learning to find clusters of inputs within which inactivity at one synapse can occlude the activity at the other synapses.",
author = "Spratling, {Michael W.} and Hayes, {Gillian M.}",
year = "2000",
month = feb,
doi = "10.1023/A:1009634821039",
language = "English",
volume = "11",
pages = "17 -- 27",
journal = "NEURAL PROCESSING LETTERS",
issn = "1370-4621",
publisher = "Springer Netherlands",
number = "1",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Learning synaptic clusters for non-linear dendritic processing

AU - Spratling, Michael W.

AU - Hayes, Gillian M.

PY - 2000/2

Y1 - 2000/2

N2 - Nonlinear dendritic processing appears to be a feature of biological neurons and would also be of use in many applications of artificial neural networks. This paper presents a model of an initially standard linear node which uses unsupervised learning to find clusters of inputs within which inactivity at one synapse can occlude the activity at the other synapses.

AB - Nonlinear dendritic processing appears to be a feature of biological neurons and would also be of use in many applications of artificial neural networks. This paper presents a model of an initially standard linear node which uses unsupervised learning to find clusters of inputs within which inactivity at one synapse can occlude the activity at the other synapses.

U2 - 10.1023/A:1009634821039

DO - 10.1023/A:1009634821039

M3 - Article

VL - 11

SP - 17

EP - 27

JO - NEURAL PROCESSING LETTERS

JF - NEURAL PROCESSING LETTERS

SN - 1370-4621

IS - 1

M1 - N/A

ER -

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