• Title/Summary/Keyword: 뉴런

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Spike Response Model and Coding of Neurons (뉴런의 스파이크 응답 모델과 코딩)

  • Lee, Ho-Suk
    • Proceedings of the Korean Information Science Society Conference
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    • 2007.06b
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    • pp.5-8
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    • 2007
  • This paper discusses the spike response model of neurons. First, this paper discusses the coding of spikes, the function of spikes, and the construction of the spikes of neurons by the superposition of simple kernel functions. This paper discusses the method of kernel superposition is general than the response of the IF (Integrate-and-Fire) neuron model, too. Next, this paper discusses the coincidence detection and the input weight computation of spiking neurons and the activity of neuron populations in some detail.

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Integrated Circuit Implementation and Characteristic Analysis of a CMOS Chaotic Neuron for Chaotic Neural Networks (카오스 신경망을 위한 CMOS 혼돈 뉴런의 집적회로 구현 및 특성 해석)

  • Song, Han-Jeong;Gwak, Gye-Dal
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.37 no.5
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    • pp.45-53
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    • 2000
  • This paper presents an analysis of the dynamical behavor in the chaotic neuron fabricated using 0.8${\mu}{\textrm}{m}$ single poly CMOS technology. An approximated empirical equation models for the sigmoid output function and chaos generative block of the chaotic neuron are extracted from the measurement data. Then the dynamical responses of the chaotic neuron such as biurcation diagram, frequency responses, Lyapunov exponent, and average firing rate are calculated with numerical analysis. In addition, we construct the chaotic neural networks which are composed of two chaotic neurons with four synapses and obtain bifurcation diagram according to synaptic weight variation. And results of experiments in the single chaotic neuron and chaotic neural networks by two neurons with the $\pm$2.5V power supply and sampling clock frequency of 10KHz are shown and compared with the simulated results.

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A Molecular Neural Network Based on Synaptic Transmission (시냅스 전위활동에 기반한 분자 신경망)

  • 정호진;조동연;장병탁
    • Proceedings of the Korean Information Science Society Conference
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    • 2003.04c
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    • pp.416-418
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    • 2003
  • 해마 뉴런의 시냅스에서 발생하는 전류는 후시냅스의 생화학적 반응을 통해 다음 뉴런으로 전달된다. 즉, 시냅스는 정보를 전달하는 매개로서 전시냅스에서 입력된 정보에 의거하여 후시냅스로 보내는 전류량을 조절하게 된다. 본 논문에서 제안하는 시냅스 기전 신경망 모델은 기존의 신경망과는 달리 시냅스에서 일어나는 반응-확산(reaction-diffusion) 모델에 의하여 입력과 출력의 관계를 결정한다. 제안된 신경망을 분류 문제에 적용한 결과 은닉 뉴런층 없이도 좋은 성능을 보였으며, 이 신경망은 앞으로 뇌에서의 생화학적 뉴런 학습 양상을 연구하는 모델로 사용될 수 있다.

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소중한 당신의 체중계_다이어트 리스타트, 뇌 컨트롤하기 - 나도 한번 따라해볼까? 연예인 다이어트

  • Kim, Nam-Hui
    • 건강소식
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    • v.36 no.10
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    • pp.10-11
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    • 2012
  • 아기를 보고 웃으면 아기도 따라 웃는다. 앞에서 누군가가 하품을 하거나 활짝 웃으면 옆 사람도 이내 따라 한다. 이런 현상은 우리 뇌의 '미러 뉴런(Mirror Neuron, 거울 신경세포)'에 의한 것이라고 한다. 미러 뉴런이란 '보는 대로 따라 하는 신경'으로 불리는데 남의 행동을 모방하도록 하는 우리 두뇌 안의 신경세포다. 그렇다면 미러 뉴런을 다이어트에 활용해보자. 유명인들의 다이어트 성공담을 들으면, '나도 ${\bigcirc}{\bigcirc}{\bigcirc}$처럼 완벽한 몸매를 가질 수 있어'라며 나의 미로 뉴런이 반응해 금세 따라하고 싶어질 것이다.

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Conversion Tools of Spiking Deep Neural Network based on ONNX (ONNX기반 스파이킹 심층 신경망 변환 도구)

  • Park, Sangmin;Heo, Junyoung
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.2
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    • pp.165-170
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    • 2020
  • The spiking neural network operates in a different mechanism than the existing neural network. The existing neural network transfers the output value to the next neuron via an activation function that does not take into account the biological mechanism for the input value to the neuron that makes up the neural network. In addition, there have been good results using deep structures such as VGGNet, ResNet, SSD and YOLO. spiking neural networks, on the other hand, operate more like the biological mechanism of real neurons than the existing activation function, but studies of deep structures using spiking neurons have not been actively conducted compared to in-depth neural networks using conventional neurons. This paper proposes the method of loading an deep neural network model made from existing neurons into a conversion tool and converting it into a spiking deep neural network through the method of replacing an existing neuron with a spiking neuron.

Design of a Silicon Neuron Circuit using a 0.18 ㎛ CMOS Process (0.18 ㎛ CMOS 공정을 이용한 실리콘 뉴런 회로 설계)

  • Han, Ye-Ji;Ji, Sung-Hyun;Yang, Hee-Sung;Lee, Soo-Hyun;Song, Han-Jung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.5
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    • pp.457-461
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    • 2014
  • Using $0.18{\mu}m$ CMOS process silicon neuron circuit of the pulse type for modeling biological neurons, were designed in the semiconductor integrated circuit. Neuron circuiSt providing is formed by MOS switch for initializing the input terminal of the capacitor to the input current signal, a pulse signal and an amplifier stage for generating an output voltage signal. Synapse circuit that can convert the current signal output of the input voltage signal, using a bump circuit consisting of NMOS transistors and PMOS few. Configure a chain of neurons for verification of the neuron model that provides synaptic neurons and two are connected in series, were performed SPICE simulation. Result of simulation, it was confirmed the normal operation of the synaptic transmission characteristics of the signal generation of nerve cells.

Neuron Tracing- and Deep Learning-guided Interactive Proofreading for Neuron Structure Segmentation (뉴런 추적 및 딥러닝 기반의 대화형 뉴런 구조 교정 기법)

  • Choi, JunYoung;Jeong, Won-Ki
    • Journal of the Korea Computer Graphics Society
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    • v.27 no.4
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    • pp.1-9
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    • 2021
  • Segmenting the compartments of neurons, such as axons, dendrites, and cell bodies, is helpful in the analysis of neurological phenomena. Recently, there have been several studies to segment the compartments through deep learning. However, this approach has the potential to include errors in the results due to noise in data and differences between training data and actual data. Therefore, in order to use these for actual analysis, it is essential to proofread the results. The proofreading process requires a lot of effort and time because an expert must perform it manually. We propose an interactive neuron structure proofreading method that can more easily correct errors in the segmentation results of a deep learning. This method proofread the neuron structure based on the characteristics of the neuron with structural consistency, so that a high-accuracy proofreading result can be obtained with less interaction.

Decreasing of Correlations Among Hidden Neurons of Multilayer Perceptrons (비선형 변환에 의한 중간층 뉴런 상관계수 감소)

  • 오상훈
    • The Journal of the Korea Contents Association
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    • v.3 no.3
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    • pp.98-102
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    • 2003
  • For elucidating the key role of hidden neurons in information processing of Multilayer perceptrons(MLPs), we prove that the correlation coefficient between weighted sums to hidden neurons decreases under element-wise nonlinear transformations. This is verified through training of MLPs for an isolated word recognition problem. From this result, we can say that the element-wise nonlinear functions reduces redundancy in the information contents of hidden neurons.

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On the Implementation of the Digital Neuron Processor (디지탈 뉴런프로세서의 구현에 관한 연구)

  • 홍봉화;이지영
    • Journal of the Korea Society of Computer and Information
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    • v.4 no.2
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    • pp.27-38
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    • 1999
  • This paper proposes a high speed digital neuron processor which uses the residue number system, making the high speed operation possible without carry propagation,. Consisting of the MAC(Multiplier and with Accumulator) operation unit, quotient operation unit and sigmoid function operation unit, the neuron processor is designed through 0.8$\mu$m CMOS fabrication. The result shows that the new implemented neuron processor can run at the speed of 19.2 nSec and the size can be reduced to 1/2 compared to the neuron processor implemented by the real number operation unit.

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A Study on the Propagation Phenomenon of Neural Stimulated Potential using Distributed Electrical Circuit (뉴런의 분포정수 회로화에 의한 자극전위의 전도현상 연구)

  • Che, Gyu-Shik
    • Journal of Advanced Navigation Technology
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    • v.15 no.2
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    • pp.256-263
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    • 2011
  • The nerve impulse is induced by the stimulation of neuron or axon, and this stimulated voltage decays along the propagation distance and time if it is subtreshold potential. This behavior can be estimated using the electrical equivalent circuit because it is very similar to propagation phenomenon of electrical circuit to which Ohm's law is applied. Therefore, I calculated various biometric parameters of body, and then analyzed the propagation behavior of stimulated potential voltage using the distributed parameters of electrical circuit in this paper.