Home
Login
Help
Contact us
Login as
Guest
Select Language
Japanese
English
XooNIps Search
ALL
Title & Keyword
Metadata
Binder
Model
Data
Stimulus
Tool
Presentation
Paper
Book
Url
Advanced
Index Tree
open all
close all
Public
To view this page, your browser must support inline frames.
Site Information
Instructions for Use
Copyrights
Privacy Policy
About us
Listing item
/
Public
/
Visiome 2004
/
Models & Theory
/
Model
/
Competitive Learning
Order by
Title
ID
Last Modified Date
Created Date
Date
▼
▲
No.Item per page
20
50
100
1 - 20 of 28 Items
PREV 1
2
NEXT
A neural network for visual pattern recognition
Fukushima Kunihiko
IEEE Computer 1988 ;21 (3) :65-75
A neural network model for selective attention in visual pattern recognition
Fukushima Kunihiko
Biological Cybernetics 1986 ;55 (1) :5-15
A neural network model for the mechanism of feature extraction: a self-organizing network with feedback inhibition
Miyake Sei , Fukushima Kunihiko
Biological Cybernetics 1984 ;50 (5) :377-384
A self-organizing neural network with a function of associative memory: Feedback-type cognitron
Fukushima Kunihiko , Miyake Sei
Biological Cybernetics 1978 ;28 (4) :201-208
Analysis of the process of visual pattern recognition by the neocognitron
Fukushima Kunihiko
Neural Networks 1989 ;2 (6) :413-420
BCM network develops orientation selectivity and ocular dominance in natural scene environment.
Shouval H , Intrator N , Cooper LN
Vision Res 1997 ;37 (23) :3339-42 [PMID:
9425548
]
Cognitron: A self-organizing multilayered neural network
Fukushima Kunihiko
Biological Cybernetics 1975 ;20 :121-136
Genetic evidence that relative synaptic efficacy biases the outcome of synaptic competition.
Buffelli M , Burgess RW , Feng G , Lobe CG , Lichtman JW , Sanes JR
Nature 2003 ;424 (6947) :430-4 [PMID:
12879071
]
Increasing robustness against background noise: visual pattern recognition by a neocognitron.
Fukushima K
Neural networks : the official journal of the International Neural Network Society 2011 ;24 (7) :767-78 [PMID:
21482455
]
Interpolating vectors for robust pattern recognition
Kunihiko FUKUSHIMA
Neural Networks 2007 ;20 (8) :904-916
Neocognitron capable of incremental learning.
Fukushima, Kunihiko
Neural networks 2004 ;17 (1) :37-46 [PMID:
14690705
]
Neocognitron for handwritten digit recognition
Fukushima Kunihiko
Neurocomputing 2003 ;51 :161-180
Neocognitron for handwritten digit recognition == Program in C language
FUKUSHIMA Kunihiko
Neocognitron on SUN workstation
Fukushima Kunihiko
Neocognitron trained with winner-kill-loser rule
Kunihiko FUKUSHIMA
Neural Networks 2010 ;23 (7) :926-938
Neocognitron with dual C-cell layers
Fukushima Kunihiko , Okada Masato , Hiroshige Kazuhito
Neural Networks 1994 ;7 (1) :41-47
Neocognitron: a hierarchical neural network capable of visual pattern recognition
Fukushima Kunihiko
Neural Networks 1988 ;1 (2) :119-130
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
Fukushima Kunihiko , Miyake Sei
Pattern Recognition 1982 ;15 (6) :455-469
Neocognitron:A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Fukushima Kunihiko
Biological Cybernetics 1980 ;36 (4) :193-202
Neural network model for selective attention in visual pattern recognition and associative recall
Fukushima Kunihiko
Applied Optics 1987 ;26 (23) :4985-4992
PREV 1
2
NEXT
Copyright (C) 2018 Neuroinformatics Unit, RIKEN Center for Brain Science