Implementing position-invariant detection of feature-conjunctions in a network of spiking neurons

S.M. Bohté; Joost N. Kok; J.A. la Poutré

doi:10.1109/IJCNN.2002.1007647

Implementing position-invariant detection of feature-conjunctions in a network of spiking neurons

S.M. Bohté, Joost N. Kok, J.A. la Poutré

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

The design of neural networks that are able to efficiently detect conjunctions of features is an important open challenge. We develop a feedforward spiking neural network that requires a constant number of neurons for detecting a conjunction irrespective of the size of the retinal input field, and for up to four simultaneously present feature-conjunctions.

Original language	English
Title of host publication	Proceedings of the 2002 IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN)
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1097-1103
Number of pages	6
ISBN (Print)	0-7803-7278-6
DOIs	https://doi.org/10.1109/IJCNN.2002.1007647
Publication status	Published - 2002
Externally published	Yes
Event	2002 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2002) - Honolulu, HI, United States Duration: 12 May 2002 → 17 May 2002

Conference

Conference	2002 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2002)
Abbreviated title	FUZZ-IEEE 2002
Country/Territory	United States
City	Honolulu, HI
Period	12/05/02 → 17/05/02

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title = "Implementing position-invariant detection of feature-conjunctions in a network of spiking neurons",

abstract = "The design of neural networks that are able to efficiently detect conjunctions of features is an important open challenge. We develop a feedforward spiking neural network that requires a constant number of neurons for detecting a conjunction irrespective of the size of the retinal input field, and for up to four simultaneously present feature-conjunctions.",

author = "S.M. Boht{\'e} and Kok, {Joost N.} and {la Poutr{\'e}}, J.A.",

year = "2002",

doi = "10.1109/IJCNN.2002.1007647",

language = "English",

isbn = "0-7803-7278-6",

pages = "1097--1103",

booktitle = "Proceedings of the 2002 IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2002 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2002), FUZZ-IEEE 2002 ; Conference date: 12-05-2002 Through 17-05-2002",

}

Bohté, SM, Kok, JN & la Poutré, JA 2002, Implementing position-invariant detection of feature-conjunctions in a network of spiking neurons. in Proceedings of the 2002 IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers, Piscataway, pp. 1097-1103, 2002 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2002), Honolulu, HI, Hawaii, United States, 12/05/02. https://doi.org/10.1109/IJCNN.2002.1007647

Implementing position-invariant detection of feature-conjunctions in a network of spiking neurons. / Bohté, S.M.; Kok, Joost N.; la Poutré, J.A.
Proceedings of the 2002 IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN). Piscataway: Institute of Electrical and Electronics Engineers, 2002. p. 1097-1103.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Implementing position-invariant detection of feature-conjunctions in a network of spiking neurons

AU - Bohté, S.M.

AU - Kok, Joost N.

AU - la Poutré, J.A.

PY - 2002

Y1 - 2002

N2 - The design of neural networks that are able to efficiently detect conjunctions of features is an important open challenge. We develop a feedforward spiking neural network that requires a constant number of neurons for detecting a conjunction irrespective of the size of the retinal input field, and for up to four simultaneously present feature-conjunctions.

AB - The design of neural networks that are able to efficiently detect conjunctions of features is an important open challenge. We develop a feedforward spiking neural network that requires a constant number of neurons for detecting a conjunction irrespective of the size of the retinal input field, and for up to four simultaneously present feature-conjunctions.

U2 - 10.1109/IJCNN.2002.1007647

DO - 10.1109/IJCNN.2002.1007647

M3 - Conference contribution

SN - 0-7803-7278-6

SP - 1097

EP - 1103

BT - Proceedings of the 2002 IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN)

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 2002 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2002)

Y2 - 12 May 2002 through 17 May 2002

ER -

Implementing position-invariant detection of feature-conjunctions in a network of spiking neurons

Abstract

Conference

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