Topological map from a photorefractive self-organizing neural network

Frauel, Yann; Galstyan, T. V.; Pauliat, Gilles; Villing, André; Roosen, Gérald

doi:10.1016/s0030-4018(96)00512-3

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…Testing the abilities of error diffusion to process gray level images, we simulated an adaptive neuron unit that has been optically implemented for experiment [1,2]. One of the adaptation laws that we used for simulations is described by the…”

Section: Simulationsmentioning

confidence: 99%

“…The simplest binarization is the direct quantization, but this method has a number of disadvantages. Error diffusion algorithm is more robust under variable illumination since it keeps the original image characteristics.Testing the abilities of error diffusion to process gray level images, we simulated an adaptive neuron unit that has been optically implemented for experiment [1,2]. It has been shown that the error diffusion is an effective way to binarize stationary gray level images for their utilization as input vectors of neural networks [3].…”

mentioning

confidence: 99%

“…Testing the abilities of error diffusion to process gray level images, we simulated an adaptive neuron unit that has been optically implemented for experiment [1,2]. It has been shown that the error diffusion is an effective way to binarize stationary gray level images for their utilization as input vectors of neural networks [3].…”

mentioning

confidence: 99%

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<title>Optimal binarization of input images for holographic neural networks</title>

Boulanov¹,

Galstian²,

Lessard³

2000

SPIE Proceedings

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The optical implementation of neural networks using volume holograms for weighted interconnections requires stable phase relation between input channels. This is particularly important for images with variable illumination. One way to solve this problem is to use binary inputs. The simplest binarization is the direct quantization, but this method has a number of disadvantages. Error diffusion algorithm is more robust under variable illumination since it keeps the original image characteristics.Testing the abilities of error diffusion to process gray level images, we simulated an adaptive neuron unit that has been optically implemented for experiment [1,2]. It has been shown that the error diffusion is an effective way to binarize stationary gray level images for their utilization as input vectors of neural networks [3]. We show in the present work that it might provide advantages as better feature extraction of the common features and fast learning convergence of neural networks even under varying illumination ofinput images during the learning process.

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Section: Simulationsmentioning

confidence: 99%

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confidence: 99%

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<title>Optimal binarization of input images for holographic neural networks</title>

Boulanov¹,

Galstian²,

Lessard³

2000

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…Therefore the use of free-space optics may be a good way to implement such systems because of the high connectivity and massive parallelism of such a system. Several optical neural networks have already been built, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] but few of them have actually achieved large capacities. [13][14][15][16] So our aim was to demonstrate the feasibility of such a system and to find the problems that are caused by high capacities.…”

Section: Introductionmentioning

confidence: 99%

“…We previously built a simple photorefractive neural network that implemented a topological map. 9 This system was rather limited, but it allowed us to gain experience and thus to design a whole new setup with a greatly enhanced capacity. In this paper we describe the improved setup and the experimental results that we obtained with it.…”

Section: Introductionmentioning

confidence: 99%