• Progress in Medical Physics
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• v.17 no.4
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• pp.260-267
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• 2006

A retinal ganglion cell's receptive field is defined as that region on the retinal surface In which a light stimulus will produce a response. A retinal ganglion cell peers out at a small patch of the visual scene through its receptive field and encodes local features with action potentials that pass through the optic nerve to higher centers. Therefore, defining the receptive field of a retinal ganglion cell is essential to understand the electrical characteristics of a ganglion cell. Distribution of receptive fields over retinal surface provides us an Insight how the retinal ganglion cell processes the visual scene. In this paper, we provide the details how to reconstruct the receptive field of a retinal ganglion cell. We recorded the ganglion cell's action potential with multielectrode array when the random checkerboard stimulus was applied. After classifying the retinal waveform Into ON-cell, OFF-cell, ON/OFF-cell, we reconstructed the receptive field of retinal ganglion cell with Matlab. Here, we show the receptive fields of ON-cell and OFF-cell.

• 대한약리학회:학술대회논문집
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• 2006.10a
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• pp.102.2-102.2
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• 2006

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.360-366
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• 2022

This paper presents a AI model structure for restoring Korean traditional palace photographs, which remain only black-and-white photographs, to color photographs using Pix2Pix, one of the adversarial generative neural network techniques. Pix2Pix consists of a combination of a synthetic image generator model and a discriminator model that determines whether a synthetic image is real or fake. This paper deals with an artificial intelligence model by adjusting a receptive field of the discriminator, and analyzes the results by considering the characteristics of the ancient palace photograph. The receptive field of Pix2Pix, which is used to restore black-and-white photographs, was commonly used in a fixed size, but a fixed size of receptive field is not suitable for a photograph which consisting with various change in an image. This paper observed the result of changing the size of the existing fixed a receptive field to identify the proper size of the discriminator that could reflect the characteristics of ancient palaces. In this experiment, the receptive field of the discriminator was adjusted based on the prepared ancient palace photos. This paper measure a loss of the model according to the change in a receptive field of the discriminator and check the results of restored photos using a well trained AI model from experiments.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.37-47
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• 2021

In this paper, we propose a new deep learning model for tongue segmentation with improved accuracy compared to the existing model by diversifying the receptive field in the U-net. Methods such as parallel convolution, dilated convolution, and constant channel increase were used to diversify the receptive field. For the proposed deep learning model, a tongue region segmentation experiment was performed on two test datasets. The training image and the test image are similar in TestSet1 and they are not in TestSet2. Experimental results show that segmentation performance improved as the receptive field was diversified. The mIoU value of the proposed method was 98.14% for TestSet1 and 91.90% for TestSet2 which was higher than the result of existing models such as U-net, DeepTongue, and TongueNet.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.319-325
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• 2022

The variation of utterance lengths is a representative factor that can degrade the performance of speaker verification systems. To handle this issue, previous studies had attempted to extract speaker features from various branches or to use convolution layers with different receptive fields. Combining the advantages of the previous two approaches for variable-length input, this paper proposes integrated receptive field diversification that extracts speaker features through more diverse receptive field. The proposed method processes the input features by convolutional layers with different receptive fields at multiple time-axis branches, and extracts speaker embedding by dynamically aggregating the processed features according to the lengths of input utterances. The deep neural networks in this study were trained on the VoxCeleb2 dataset and tested on the VoxCeleb1 evaluation dataset that divided into 1 s, 2 s, 5 s, and full-length. Experimental results demonstrated that the proposed method reduces the equal error rate by 19.7 % compared to the baseline.

• The Korean Journal of Physiology and Pharmacology
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• v.2 no.1
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• pp.21-30
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• 1998

The effects of GABA and glutamate on the horizontal cells were explored by an intracellular recording method to discern the mechanisms of receptive field formation by chemical coupling in the catfish outer retina. The results suggest that the horizontal cells of the catfish retina might use GABA as their transmitters and that the GABAergic system contributes to the formation of receptive fields of the horizontal cells. GABAC receptors may be involved in a chemical coupling between horizontal cells and concerned with the depolarizing actions by GABA on horizontal cells in the catfish retina. Since the chloride equilibrium potential is more positive than the dark membrane potential in horizontal cells, GABA released from a horizontal cell may depolarize the neighboring horizontal cells. Thus a chemical coupling between horizontal cells may be formed. $GABA_A$ receptors also may be involved in the negative feedback mechanism between photoreceptor and horizontal cell. And glutamate may be involved in connecting positive and negative feedback systems since it potentiated the GABA's actions. Therefore, it is presumed that large receptive fields in the catfish retina are formed not only by electrical coupling but also by chemical coupling between horizontal cells. And information travels laterally by pathways involving both electrical coupling composed of gap junctions and chemical coupling in the retinal network.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.436-439
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• 2007

Noise reduction in the image is very important to improve the quality of the image. This paper discusses a new noise reduction technique which uses the On/Off spatio-temporal structure of the receptive fields. Also this paper proposes a structurally improved artificial vision system which incorporates the sphere type retina structure, an improved On/Off spatio-temporal receptive fields structure, and chiasm for hemianopia testing.

• The Transactions of the Korea Information Processing Society
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• v.1 no.3
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• pp.311-318
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• 1994

In this paper, a new stroke extraction method of Chinese character base on the human optical field(the Receptive Field of Cell) is proposed. In processing the feature extraction of the chinese character, needed are more perfect extraction methods for separated informations and its data base. This method can be applied to processing neural cell using conventional feature extraction mechanism in the optical boundary of retina and cerebrum. With this method, its applicability and effectiveness were demonstrated extracting strokes from Chinese character.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.620-623
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• 2003

The TAM (Topographic Attentive Mapping) network is a biologically-motivated neural network. Fuzzy rules are acquired from the TAM network by the pruning algorithm. In this paper we formulate a new input layer using Gabor function for TAU network to realize receptive field of human visual cortex.

• Smart Structures and Systems
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• v.32 no.2
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• pp.111-121
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• 2023

This study proposes an automated assembly performance evaluation method for prefabricated steel structures (PSSs) using machine learning methods. Assembly component images were segmented using a modified version of the receptive field pyramid. By factorizing channel modulation and the receptive field exploration layers of the convolution pyramid, highly accurate segmentation results were obtained. After completing segmentation, the positions of the bolt holes were calculated using various image processing techniques, such as fuzzy-based edge detection, Hough's line detection, and image perspective transformation. By calculating the distance ratio between bolt holes, the assembly performance of the PSS was estimated using the k-nearest neighbors (kNN) algorithm. The effectiveness of the proposed framework was validated using a 3D PSS printing model and a field test. The results indicated that this approach could recognize assembly components with an intersection over union (IoU) of 95% and evaluate assembly performance with an error of less than 5%.